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|
<?xml version="1.0" encoding="ISO-8859-1"?>
<?xml-stylesheet type="text/xsl" href="stylesheet.xsl"?>
<doc>
<title>Machine Learning</title>
<!-- ************************************************************************* -->
<body>
<a href="ml_guide.svg"><img src="ml_guide.svg" width="100%"/></a>
<br/>
<br/>
<p><font style='font-size:1.4em;line-height:1.1em'>
Dlib contains a wide range of machine learning algorithms. All
designed to be highly modular, quick to execute, and simple to use
via a clean and modern C++ API. It is used in a wide range of
applications including robotics, embedded devices, mobile phones, and large
high performance computing environments. If you use dlib in your
research please cite:
</font></p>
<pre>
Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09a.pdf">Dlib-ml: A Machine Learning Toolkit</a>.
<i>Journal of Machine Learning Research</i>, 2009
@Article{dlib09,
author = {Davis E. King},
title = {Dlib-ml: A Machine Learning Toolkit},
journal = {Journal of Machine Learning Research},
year = {2009},
volume = {10},
pages = {1755-1758},
}
</pre>
</body>
<!-- ************************************************************************* -->
<menu width="150">
<top>
<center><h2><u>Primary Algorithms</u></h2></center>
<section>
<name>Binary Classification</name>
<item>svm_nu_trainer</item>
<item>svm_c_trainer</item>
<item>svm_c_linear_trainer</item>
<item>svm_c_linear_dcd_trainer</item>
<item>svm_c_ekm_trainer</item>
<item>rvm_trainer</item>
<item>svm_pegasos</item>
<item>train_probabilistic_decision_function</item>
</section>
<section>
<name>Multiclass Classification</name>
<item>one_vs_one_trainer</item>
<item>one_vs_all_trainer</item>
<item>svm_multiclass_linear_trainer</item>
</section>
<section>
<name>Regression</name>
<item>mlp</item>
<item>krls</item>
<item>rls</item>
<item>krr_trainer</item>
<item>rr_trainer</item>
<item>svr_trainer</item>
<item>svr_linear_trainer</item>
<item>rvm_regression_trainer</item>
<item>rbf_network_trainer</item>
<item>random_forest_regression_trainer</item>
</section>
<section>
<name>Structured Prediction</name>
<item nolink="true">
<name>Problem Instances</name>
<sub>
<item>structural_svm_sequence_labeling_problem</item>
<item>structural_svm_object_detection_problem</item>
<item>structural_svm_assignment_problem</item>
<item>structural_svm_graph_labeling_problem</item>
</sub>
</item>
<item nolink="true">
<name>Core Tools</name>
<sub>
<item>structural_svm_problem</item>
<item>structural_svm_problem_threaded</item>
<item>svm_struct_controller_node</item>
<item>svm_struct_processing_node</item>
</sub>
</item>
<item>structural_object_detection_trainer</item>
<item>structural_sequence_labeling_trainer</item>
<item>structural_sequence_segmentation_trainer</item>
<item>structural_assignment_trainer</item>
<item>structural_track_association_trainer</item>
<item>structural_graph_labeling_trainer</item>
<item>svm_rank_trainer</item>
<item>shape_predictor_trainer</item>
</section>
<section>
<name>Deep Learning</name>
<item nolink="true">
<name>Core Tools</name>
<sub>
<item>dnn_trainer</item>
<item>add_layer</item>
<item>add_loss_layer</item>
<item>repeat</item>
<item>add_tag_layer</item>
<item>add_skip_layer</item>
<item>layer</item>
<item>test_layer</item>
<item>resizable_tensor</item>
<item>alias_tensor</item>
</sub>
</item>
<item nolink="true">
<name>Input Layers</name>
<sub>
<item>input</item>
<item>input_rgb_image</item>
<item>input_rgb_image_sized</item>
<item>input_rgb_image_pyramid</item>
<item>
<name>EXAMPLE_INPUT_LAYER</name>
<link>dlib/dnn/input_abstract.h.html#EXAMPLE_INPUT_LAYER</link>
</item>
</sub>
</item>
<item nolink="true">
<name>Computational Layers</name>
<sub>
<item>
<name>EXAMPLE_COMPUTATIONAL_LAYER</name>
<link>dlib/dnn/layers_abstract.h.html#EXAMPLE_COMPUTATIONAL_LAYER_</link>
</item>
<item>
<name>fc</name>
<link>dlib/dnn/layers_abstract.h.html#fc_</link>
</item>
<item>
<name>con</name>
<link>dlib/dnn/layers_abstract.h.html#con_</link>
</item>
<item>
<name>cont</name>
<link>dlib/dnn/layers_abstract.h.html#cont_</link>
</item>
<item>
<name>scale</name>
<link>dlib/dnn/layers_abstract.h.html#scale_</link>
</item>
<item>
<name>extract</name>
<link>dlib/dnn/layers_abstract.h.html#extract_</link>
</item>
<item>
<name>mult_prev</name>
<link>dlib/dnn/layers_abstract.h.html#mult_prev_</link>
</item>
<item>
<name>upsample</name>
<link>dlib/dnn/layers_abstract.h.html#upsample_</link>
</item>
<item>
<name>l2normalize</name>
<link>dlib/dnn/layers_abstract.h.html#l2normalize_</link>
</item>
<item>
<name>dropout</name>
<link>dlib/dnn/layers_abstract.h.html#dropout_</link>
</item>
<item>
<name>multiply</name>
<link>dlib/dnn/layers_abstract.h.html#multiply_</link>
</item>
<item>
<name>bn</name>
<link>dlib/dnn/layers_abstract.h.html#bn_</link>
</item>
<item>
<name>affine</name>
<link>dlib/dnn/layers_abstract.h.html#affine_</link>
</item>
<item>
<name>max_pool</name>
<link>dlib/dnn/layers_abstract.h.html#max_pool_</link>
</item>
<item>
<name>avg_pool</name>
<link>dlib/dnn/layers_abstract.h.html#avg_pool_</link>
</item>
<item>
<name>relu</name>
<link>dlib/dnn/layers_abstract.h.html#relu_</link>
</item>
<item>
<name>concat</name>
<link>dlib/dnn/layers_abstract.h.html#concat_</link>
</item>
<item>
<name>prelu</name>
<link>dlib/dnn/layers_abstract.h.html#prelu_</link>
</item>
<item>
<name>sig</name>
<link>dlib/dnn/layers_abstract.h.html#sig_</link>
</item>
<item>
<name>htan</name>
<link>dlib/dnn/layers_abstract.h.html#htan_</link>
</item>
<item>
<name>softmax_all</name>
<link>dlib/dnn/layers_abstract.h.html#softmax_all_</link>
</item>
<item>
<name>softmax</name>
<link>dlib/dnn/layers_abstract.h.html#softmax_</link>
</item>
<item>
<name>add_prev</name>
<link>dlib/dnn/layers_abstract.h.html#add_prev_</link>
</item>
<item>
<name>inception</name>
<link>dlib/dnn/layers_abstract.h.html#inception</link>
</item>
</sub>
</item>
<item nolink="true">
<name>Loss Layers</name>
<sub>
<item>
<name>EXAMPLE_LOSS_LAYER</name>
<link>dlib/dnn/loss_abstract.h.html#EXAMPLE_LOSS_LAYER_</link>
</item>
<item>
<name>loss_dot</name>
<link>dlib/dnn/loss_abstract.h.html#loss_dot_</link>
</item>
<item>
<name>loss_epsilon_insensitive</name>
<link>dlib/dnn/loss_abstract.h.html#loss_epsilon_insensitive_</link>
</item>
<item>
<name>loss_ranking</name>
<link>dlib/dnn/loss_abstract.h.html#loss_ranking_</link>
</item>
<item>
<name>loss_binary_hinge</name>
<link>dlib/dnn/loss_abstract.h.html#loss_binary_hinge_</link>
</item>
<item>
<name>loss_binary_log</name>
<link>dlib/dnn/loss_abstract.h.html#loss_binary_log_</link>
</item>
<item>
<name>loss_multimulticlass_log</name>
<link>dlib/dnn/loss_abstract.h.html#loss_multimulticlass_log_</link>
</item>
<item>
<name>loss_multiclass_log</name>
<link>dlib/dnn/loss_abstract.h.html#loss_multiclass_log_</link>
</item>
<item>
<name>loss_multiclass_log_per_pixel</name>
<link>dlib/dnn/loss_abstract.h.html#loss_multiclass_log_per_pixel_</link>
</item>
<item>
<name>loss_multiclass_log_per_pixel_weighted</name>
<link>dlib/dnn/loss_abstract.h.html#loss_multiclass_log_per_pixel_weighted_</link>
</item>
<item>
<name>loss_mmod</name>
<link>#loss_mmod_</link>
</item>
<item>
<name>loss_metric</name>
<link>#loss_metric_</link>
</item>
<item>
<name>loss_mean_squared</name>
<link>#loss_mean_squared_</link>
</item>
<item>
<name>loss_mean_squared_per_pixel</name>
<link>dlib/dnn/loss_abstract.h.html#loss_mean_squared_per_pixel_</link>
</item>
<item>
<name>loss_mean_squared_multioutput</name>
<link>dlib/dnn/loss_abstract.h.html#loss_mean_squared_multioutput_</link>
</item>
</sub>
</item>
<item nolink="true">
<name>Solvers</name>
<sub>
<item>
<name>EXAMPLE_SOLVER</name>
<link>dlib/dnn/solvers_abstract.h.html#EXAMPLE_SOLVER</link>
</item>
<item>
<name>sgd</name>
<link>dlib/dnn/solvers_abstract.h.html#sgd</link>
</item>
<item>
<name>adam</name>
<link>dlib/dnn/solvers_abstract.h.html#adam</link>
</item>
</sub>
</item>
</section>
<section>
<name>Clustering</name>
<item>pick_initial_centers</item>
<item>kkmeans</item>
<item>find_clusters_using_kmeans</item>
<item>find_clusters_using_angular_kmeans</item>
<item>nearest_center</item>
<item>newman_cluster</item>
<item>spectral_cluster</item>
<item>chinese_whispers</item>
<item>bottom_up_cluster</item>
<item>segment_number_line</item>
<item>modularity</item>
</section>
<section>
<name>Unsupervised</name>
<item>kcentroid</item>
<item>linearly_independent_subset_finder</item>
<item>empirical_kernel_map</item>
<item>svm_one_class_trainer</item>
<item>vector_normalizer</item>
<item>vector_normalizer_pca</item>
<item>sammon_projection</item>
<item>cca</item>
</section>
<section>
<name>Semi-Supervised/Metric Learning</name>
<item>linear_manifold_regularizer</item>
<item>discriminant_pca</item>
<item>vector_normalizer_frobmetric</item>
<item>compute_lda_transform</item>
</section>
<section>
<name>Reinforcement Learning</name>
<item>lspi</item>
</section>
<section>
<name>Feature Selection</name>
<item>rank_features</item>
<item>sort_basis_vectors</item>
<item>rank_unlabeled_training_samples</item>
</section>
<center><h2><u>Other Tools</u></h2></center>
<section>
<name>Validation</name>
<item>cross_validate_trainer</item>
<item>cross_validate_object_detection_trainer</item>
<item>cross_validate_trainer_threaded</item>
<item>cross_validate_multiclass_trainer</item>
<item>cross_validate_regression_trainer</item>
<item>cross_validate_sequence_labeler</item>
<item>cross_validate_sequence_segmenter</item>
<item>cross_validate_assignment_trainer</item>
<item>cross_validate_track_association_trainer</item>
<item>cross_validate_graph_labeling_trainer</item>
<item>cross_validate_ranking_trainer</item>
<item>test_binary_decision_function</item>
<item>test_multiclass_decision_function</item>
<item>test_regression_function</item>
<item>test_object_detection_function</item>
<item>test_sequence_labeler</item>
<item>test_sequence_segmenter</item>
<item>test_assignment_function</item>
<item>test_track_association_function</item>
<item>test_graph_labeling_function</item>
<item>test_ranking_function</item>
<item>test_shape_predictor</item>
<item>average_precision</item>
<item>equal_error_rate</item>
<item>compute_roc_curve</item>
</section>
<section>
<name>Trainer Adapters</name>
<item>reduced</item>
<item>reduced2</item>
<item>batch</item>
<item>probabilistic</item>
<item>verbose_batch</item>
<item>batch_cached</item>
<item>verbose_batch_cached</item>
<item>null_trainer</item>
<item>roc_c1_trainer</item>
<item>roc_c2_trainer</item>
</section>
<section>
<name>Kernels</name>
<item>radial_basis_kernel</item>
<item>polynomial_kernel</item>
<item>sigmoid_kernel</item>
<item>linear_kernel</item>
<item>histogram_intersection_kernel</item>
<item>offset_kernel</item>
<item>sparse_radial_basis_kernel</item>
<item>sparse_polynomial_kernel</item>
<item>sparse_sigmoid_kernel</item>
<item>sparse_linear_kernel</item>
<item>sparse_histogram_intersection_kernel</item>
</section>
<section>
<name>Function Objects</name>
<item>random_forest_regression_function</item>
<item>decision_function</item>
<item>projection_function</item>
<item>distance_function</item>
<item>probabilistic_decision_function</item>
<item>probabilistic_function</item>
<item>normalized_function</item>
<item>one_vs_one_decision_function</item>
<item>multiclass_linear_decision_function</item>
<item>one_vs_all_decision_function</item>
<item>sequence_labeler</item>
<item>sequence_segmenter</item>
<item>assignment_function</item>
<item>track_association_function</item>
<item>graph_labeler</item>
<item>policy</item>
</section>
<section>
<name>Data IO</name>
<item>load_image_dataset_metadata</item>
<item>load_image_dataset</item>
<item>save_image_dataset_metadata</item>
<item>load_libsvm_formatted_data</item>
<item>save_libsvm_formatted_data</item>
<item>fix_nonzero_indexing</item>
<item>make_bounding_box_regression_training_data</item>
</section>
<section>
<name>Miscellaneous</name>
<item>simplify_linear_decision_function</item>
<item>fill_lisf</item>
<item>randomize_samples</item>
<item>is_binary_classification_problem</item>
<item>is_sequence_labeling_problem</item>
<item>is_sequence_segmentation_problem</item>
<item>is_graph_labeling_problem</item>
<item>is_assignment_problem</item>
<item>is_track_association_problem</item>
<item>is_forced_assignment_problem</item>
<item>approximate_distance_function</item>
<item>is_learning_problem</item>
<item>select_all_distinct_labels</item>
<item>find_gamma_with_big_centroid_gap</item>
<item>compute_mean_squared_distance</item>
<item>kernel_matrix</item>
<item>ranking_pair</item>
<item>is_ranking_problem</item>
<item>count_ranking_inversions</item>
<item>learn_platt_scaling</item>
<item>process_sample</item>
</section>
</top>
</menu>
<!-- ************************************************************************* -->
<!-- ************************************************************************* -->
<!-- ************************************************************************* -->
<components>
<!-- ************************************************************************* -->
<component>
<name>add_layer</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/core_abstract.h</spec_file>
<description>
In dlib, a deep neural network is composed of 3 main parts. An
<a href="dlib/dnn/input_abstract.h.html#EXAMPLE_INPUT_LAYER">input layer</a>, a bunch of
<a href="dlib/dnn/layers_abstract.h.html#EXAMPLE_COMPUTATIONAL_LAYER_">computational layers</a>,
and optionally a
<a href="dlib/dnn/loss_abstract.h.html#EXAMPLE_LOSS_LAYER_">loss layer</a>. The add_layer
class is the central object which adds a computational layer onto an
input layer or an entire network. Therefore, deep neural networks are created
by stacking many layers on top of each other using the add_layer class.
<p>
For a tutorial showing how this is accomplished read
the <a href="dnn_introduction_ex.cpp.html">DNN Introduction part 1</a> and
<a href="dnn_introduction2_ex.cpp.html">DNN Introduction part 2</a>.
</p>
</description>
<examples>
<example>dnn_introduction_ex.cpp.html</example>
<example>dnn_introduction2_ex.cpp.html</example>
<example>dnn_inception_ex.cpp.html</example>
<example>dnn_imagenet_ex.cpp.html</example>
<example>dnn_imagenet_train_ex.cpp.html</example>
<example>dnn_mmod_ex.cpp.html</example>
<example>dnn_mmod_find_cars_ex.cpp.html</example>
<example>dnn_mmod_find_cars2_ex.cpp.html</example>
<example>dnn_mmod_train_find_cars_ex.cpp.html</example>
<example>dnn_mmod_face_detection_ex.cpp.html</example>
<example>dnn_mmod_dog_hipsterizer.cpp.html</example>
<example>dnn_metric_learning_ex.cpp.html</example>
<example>dnn_metric_learning_on_images_ex.cpp.html</example>
<example>dnn_face_recognition_ex.cpp.html</example>
<example>dnn_semantic_segmentation_ex.cpp.html</example>
<example>dnn_semantic_segmentation_train_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>dnn_trainer</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/trainer_abstract.h</spec_file>
<description>
This object is a tool training a deep neural network.
<p>
For a tutorial showing how this is accomplished read
the <a href="dnn_introduction_ex.cpp.html">DNN Introduction part 1</a> and
<a href="dnn_introduction2_ex.cpp.html">DNN Introduction part 2</a>.
</p>
</description>
<examples>
<example>dnn_introduction_ex.cpp.html</example>
<example>dnn_introduction2_ex.cpp.html</example>
<example>dnn_inception_ex.cpp.html</example>
<example>dnn_imagenet_ex.cpp.html</example>
<example>dnn_imagenet_train_ex.cpp.html</example>
<example>dnn_mmod_ex.cpp.html</example>
<example>dnn_mmod_train_find_cars_ex.cpp.html</example>
<example>dnn_metric_learning_ex.cpp.html</example>
<example>dnn_metric_learning_on_images_ex.cpp.html</example>
<example>dnn_semantic_segmentation_train_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>add_loss_layer</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/core_abstract.h</spec_file>
<description>
This object is a tool for stacking a <a href="dlib/dnn/loss_abstract.h.html#EXAMPLE_LOSS_LAYER_">loss layer</a>
on the top of a deep neural network.
</description>
<examples>
<example>dnn_introduction_ex.cpp.html</example>
<example>dnn_introduction2_ex.cpp.html</example>
<example>dnn_inception_ex.cpp.html</example>
<example>dnn_imagenet_ex.cpp.html</example>
<example>dnn_imagenet_train_ex.cpp.html</example>
<example>dnn_mmod_ex.cpp.html</example>
<example>dnn_mmod_find_cars_ex.cpp.html</example>
<example>dnn_mmod_train_find_cars_ex.cpp.html</example>
<example>dnn_metric_learning_ex.cpp.html</example>
<example>dnn_metric_learning_on_images_ex.cpp.html</example>
<example>dnn_face_recognition_ex.cpp.html</example>
<example>dnn_mmod_face_detection_ex.cpp.html</example>
<example>dnn_mmod_dog_hipsterizer.cpp.html</example>
<example>dnn_semantic_segmentation_train_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>repeat</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/core_abstract.h</spec_file>
<description>
This object adds N copies of a computational layer onto a deep neural network.
It is essentially the same as using <a href="#add_layer">add_layer</a> N times,
except that it involves less typing, and for large N, will compile much faster.
</description>
<examples>
<example>dnn_introduction2_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>add_tag_layer</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/core_abstract.h</spec_file>
<description>
This object is a tool for tagging layers in a deep neural network. These tags make it
easy to refer to the tagged layer in other parts of your code.
Specifically, this object adds a new layer onto a deep neural network.
However, this layer simply performs the identity transform.
This means it is a no-op and its presence does not change the
behavior of the network. It exists solely to be used by <a
href="#add_skip_layer">add_skip_layer</a> or <a href="#layer">layer()</a> to reference a
particular part of a network.
<p>
For a tutorial showing how to use tagging see the
<a href="dnn_introduction2_ex.cpp.html">dnn_introduction2_ex.cpp</a>
example program.
</p>
</description>
<examples>
<example>dnn_introduction2_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>add_skip_layer</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/core_abstract.h</spec_file>
<description>
This object adds a new layer to a deep neural network which draws its input
from a <a href="#add_tag_layer">tagged layer</a> rather than from
the immediate predecessor layer as is normally done.
<p>
For a tutorial showing how to use tagging see the
<a href="dnn_introduction2_ex.cpp.html">dnn_introduction2_ex.cpp</a>
example program.
</p>
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>layer</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/core_abstract.h</spec_file>
<description>
This global function references a <a href="#add_tag_layer">tagged layer</a>
inside a deep neural network object.
<p>
For a tutorial showing how to use tagging see the
<a href="dnn_introduction2_ex.cpp.html">dnn_introduction2_ex.cpp</a>
example program.
</p>
</description>
<examples>
<example>dnn_introduction2_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>input</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/input_abstract.h</spec_file>
<description>
This is a simple input layer type for use in a deep neural network which
takes some kind of image as input and loads it into a network.
</description>
<examples>
<example>dnn_introduction_ex.cpp.html</example>
<example>dnn_introduction2_ex.cpp.html</example>
<example>dnn_inception_ex.cpp.html</example>
<example>dnn_imagenet_ex.cpp.html</example>
<example>dnn_imagenet_train_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>input_rgb_image</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/input_abstract.h</spec_file>
<description>
This is a simple input layer type for use in a deep neural network
which takes an RGB image as input and loads it into a network. It
is very similar to the <a href="#input">input layer</a> except that
it allows you to subtract the average color value from each color
channel when converting an image to a tensor.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>input_rgb_image_sized</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/input_abstract.h</spec_file>
<description>
This layer has an interface and behavior identical to <a href="#input_rgb_image">input_rgb_image</a>
except that it requires input images to have a particular size.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>input_rgb_image_pyramid</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/input_abstract.h</spec_file>
<description>
This input layer works with RGB images of type <tt>matrix<rgb_pixel></tt>. It is
identical to <a href="#input_rgb_image">input_rgb_image</a> except that it
outputs a tensor containing a <a href="imaging.html#create_tiled_pyramid">tiled image pyramid</a>
of each input image rather than a simple copy of each image.
This input layer is meant to be used with a loss layer such as the <a href="#loss_mmod_">MMOD loss layer</a>.
</description>
<examples>
<example>dnn_mmod_ex.cpp.html</example>
<example>dnn_mmod_find_cars_ex.cpp.html</example>
<example>dnn_mmod_find_cars2_ex.cpp.html</example>
<example>dnn_mmod_train_find_cars_ex.cpp.html</example>
<example>dnn_mmod_face_detection_ex.cpp.html</example>
<example>dnn_mmod_dog_hipsterizer.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>loss_mmod_</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/loss_abstract.h</spec_file>
<description>
This object is a <a href="dlib/dnn/loss_abstract.h.html#EXAMPLE_LOSS_LAYER_">loss layer</a>
for a deep neural network. In particular, it implements the Max Margin Object Detection
loss defined in the paper:
<blockquote><a href="http://arxiv.org/abs/1502.00046">Max-Margin Object Detection</a> by Davis E. King.</blockquote>
This means you use this loss if you want to detect the locations of objects
in images. For example, here are some videos that uses loss_mmod to find cars:
<center><youtube src="https://www.youtube.com/embed/4B3bzmxMAZU"/></center>
<br/>
<center><youtube src="https://www.youtube.com/embed/OHbJ7HhbG74"/></center>
</description>
<examples>
<example>dnn_mmod_ex.cpp.html</example>
<example>dnn_mmod_find_cars_ex.cpp.html</example>
<example>dnn_mmod_find_cars2_ex.cpp.html</example>
<example>dnn_mmod_train_find_cars_ex.cpp.html</example>
<example>dnn_mmod_face_detection_ex.cpp.html</example>
<example>dnn_mmod_dog_hipsterizer.cpp.html</example>
<example>cnn_face_detector.py.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>loss_metric_</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/loss_abstract.h</spec_file>
<description>
This object is a <a href="dlib/dnn/loss_abstract.h.html#EXAMPLE_LOSS_LAYER_">loss layer</a>
for a deep neural network. In particular, it allows you to learn to map objects
into a vector space where objects sharing the same class label are close to
each other, while objects with different labels are far apart.
</description>
<examples>
<example>dnn_metric_learning_ex.cpp.html</example>
<example>dnn_metric_learning_on_images_ex.cpp.html</example>
<example>dnn_face_recognition_ex.cpp.html</example>
<example>face_recognition.py.html</example>
<example>face_clustering.py.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>loss_mean_squared_</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/loss_abstract.h</spec_file>
<description>
This object is a <a href="dlib/dnn/loss_abstract.h.html#EXAMPLE_LOSS_LAYER_">loss layer</a>
for a deep neural network. In particular, it implements the mean squared loss, which is
appropriate for regression problems.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>loss_mean_squared_multioutput_</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/loss_abstract.h</spec_file>
<description>
This object is a <a href="dlib/dnn/loss_abstract.h.html#EXAMPLE_LOSS_LAYER_">loss layer</a>
for a deep neural network. In particular, it implements the mean squared loss, which is
appropriate for regression problems. It is identical to the <a href="#loss_mean_squared_">loss_mean_squared_</a>
loss except this version supports multiple output values.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>test_layer</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/core_abstract.h</spec_file>
<description>
This is a function which tests if a layer object correctly implements
the <a href="dlib/dnn/layers_abstract.h.html#EXAMPLE_COMPUTATIONAL_LAYER_">documented contract</a>
for a computational layer in a deep neural network.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>resizable_tensor</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/tensor_abstract.h</spec_file>
<description>
This object represents a 4D array of float values, all stored contiguously
in memory. Importantly, it keeps two copies of the floats, one on the host
CPU side and another on the GPU device side. It automatically performs the
necessary host/device transfers to keep these two copies of the data in
sync.
<p>
All transfers to the device happen asynchronously with respect to the
default CUDA stream so that CUDA kernel computations can overlap with data
transfers. However, any transfers from the device to the host happen
synchronously in the default CUDA stream. Therefore, you should perform
all your CUDA kernel launches on the default stream so that transfers back
to the host do not happen before the relevant computations have completed.
</p>
<p>
If DLIB_USE_CUDA is not #defined then this object will not use CUDA at all.
Instead, it will simply store one host side memory block of floats.
</p>
<p>
Finally, the convention in dlib code is to interpret the tensor as a set of
num_samples() 3D arrays, each of dimension k() by nr() by nc(). Also,
while this class does not specify a memory layout, the convention is to
assume that indexing into an element at coordinates (sample,k,nr,nc) can be
accomplished via:
<tt>host()[((sample*t.k() + k)*t.nr() + nr)*t.nc() + nc]</tt>
</p>
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>alias_tensor</name>
<file>dlib/dnn.h</file>
<spec_file link="true">dlib/dnn/tensor_abstract.h</spec_file>
<description>
This object is a <a href="#resizable_tensor">tensor</a> that
aliases another tensor. That is, it doesn't have its own block of
memory but instead simply holds pointers to the memory of another
tensor object. It therefore allows you to efficiently break a tensor
into pieces and pass those pieces into functions.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>modularity</name>
<file>dlib/clustering.h</file>
<spec_file link="true">dlib/clustering/modularity_clustering_abstract.h</spec_file>
<description>
This function computes the modularity of a particular graph clustering. This
is a number that tells you how good the clustering is. In particular, it
is the measure optimized by the <a href="#newman_cluster">newman_cluster</a>
routine.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>newman_cluster</name>
<file>dlib/clustering.h</file>
<spec_file link="true">dlib/clustering/modularity_clustering_abstract.h</spec_file>
<description>
This function performs the clustering algorithm described in the paper
<blockquote>Modularity and community structure in networks by M. E. J. Newman.</blockquote>
In particular, this is a method for automatically clustering the nodes in a
graph into groups. The method is able to automatically determine the number
of clusters and does not have any parameters. In general, it is a very good
clustering technique.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>spectral_cluster</name>
<file>dlib/clustering.h</file>
<spec_file link="true">dlib/clustering/spectral_cluster_abstract.h</spec_file>
<description>
This function performs the clustering algorithm described in the paper
<blockquote>On spectral clustering: Analysis and an algorithm by Ng, Jordan, and Weiss.</blockquote>
</description>
<examples>
<example>kkmeans_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>bottom_up_cluster</name>
<file>dlib/clustering.h</file>
<spec_file link="true">dlib/clustering/bottom_up_cluster_abstract.h</spec_file>
<description>
This function runs a bottom up agglomerative clustering algorithm.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>segment_number_line</name>
<file>dlib/clustering.h</file>
<spec_file link="true">dlib/clustering/bottom_up_cluster_abstract.h</spec_file>
<description>
This routine clusters real valued scalars in essentially linear time.
It uses a combination of bottom up clustering and a simple greedy scan
to try and find the most compact set of ranges that contain all
given scalar values.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>chinese_whispers</name>
<file>dlib/clustering.h</file>
<spec_file link="true">dlib/clustering/chinese_whispers_abstract.h</spec_file>
<description>
This function performs the clustering algorithm described in the paper
<blockquote>Chinese Whispers - an Efficient Graph Clustering Algorithm and its
Application to Natural Language Processing Problems by Chris Biemann.</blockquote>
In particular, this is a method for automatically clustering the nodes in a
graph into groups. The method is able to automatically determine the number
of clusters.
</description>
<examples>
<example>dnn_face_recognition_ex.cpp.html</example>
<example>face_clustering.py.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>find_clusters_using_kmeans</name>
<file>dlib/clustering.h</file>
<spec_file link="true">dlib/svm/kkmeans_abstract.h</spec_file>
<description>
This is a simple linear kmeans clustering implementation.
It uses Euclidean distance to compare samples.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>find_clusters_using_angular_kmeans</name>
<file>dlib/clustering.h</file>
<spec_file link="true">dlib/svm/kkmeans_abstract.h</spec_file>
<description>
This is a simple linear kmeans clustering implementation.
To compare a sample to a cluster, it measures the angle between them
with respect to the origin. Therefore, it tries to find clusters
of points that all have small angles between each cluster member.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>nearest_center</name>
<file>dlib/clustering.h</file>
<spec_file link="true">dlib/svm/kkmeans_abstract.h</spec_file>
<description>
This function takes a list of cluster centers and a query vector
and identifies which cluster center is nearest to the query vector.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>pick_initial_centers</name>
<file>dlib/clustering.h</file>
<spec_file link="true">dlib/svm/kkmeans_abstract.h</spec_file>
<description>
This is a function that you can use to seed data clustering algorithms
like the <a href="#kkmeans">kkmeans</a> clustering method. What it
does is pick reasonable starting points for clustering by basically
trying to find a set of points that are all far away from each other.
</description>
<examples>
<example>kkmeans_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>ranking_pair</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/ranking_tools_abstract.h</spec_file>
<description>
This object is used to contain a ranking example. Therefore, ranking_pair
objects are used to represent training examples for learning-to-rank tasks,
such as those used by the <a href="#svm_rank_trainer">svm_rank_trainer</a>.
</description>
<examples>
<example>svm_rank_ex.cpp.html</example>
<example>svm_rank.py.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>kernel_matrix</name>
<file>dlib/svm.h</file>
<spec_file>dlib/svm/kernel_matrix_abstract.h</spec_file>
<description>
This is a simple set of functions that makes it easy to turn a kernel
object and a set of samples into a kernel matrix. It takes these two
things and returns a <a href="dlib/matrix/matrix_exp_abstract.h.html#matrix_exp">matrix expression</a>
that represents the kernel matrix.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>is_ranking_problem</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/ranking_tools_abstract.h</spec_file>
<description>
This function takes a set of training data for a learning-to-rank problem
and reports back if it could possibly be a well formed problem.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>count_ranking_inversions</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/ranking_tools_abstract.h</spec_file>
<description>
Given two sets of objects, X and Y, and an ordering relationship defined
between their elements, this function counts how many times we see an element
in the set Y ordered before an element in the set X. Additionally, this
routine executes efficiently in O(n*log(n)) time via the use of quick sort.
</description>
</component>
<!-- ************************************************************************* -->
<component checked="true">
<name>mlp</name>
<file>dlib/mlp.h</file>
<spec_file>dlib/mlp/mlp_kernel_abstract.h</spec_file>
<description>
<p>
This object represents a multilayer layer perceptron network that is
trained using the back propagation algorithm. The training algorithm also
incorporates the momentum method. That is, each round of back propagation
training also adds a fraction of the previous update. This fraction
is controlled by the momentum term set in the constructor.
</p>
<p>
It is worth noting that a MLP is, in general, very inferior to modern
kernel algorithms such as the support vector machine. So if you haven't
tried any other techniques with your data you really should.
</p>
</description>
<examples>
<example>mlp_ex.cpp.html</example>
</examples>
<implementations>
<implementation>
<name>mlp_kernel_1</name>
<file>dlib/mlp/mlp_kernel_1.h</file>
<description>
This is implemented in the obvious way.
</description>
<typedefs>
<typedef>
<name>kernel_1a</name>
<description>is a typedef for mlp_kernel_1</description>
</typedef>
</typedefs>
</implementation>
</implementations>
</component>
<!-- ************************************************************************* -->
<component>
<name>krls</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/krls_abstract.h</spec_file>
<description>
This is an implementation of the kernel recursive least squares algorithm
described in the paper The Kernel Recursive Least Squares Algorithm by Yaakov Engel.
<p>
The long and short of this algorithm is that it is an online kernel based
regression algorithm. You give it samples (x,y) and it learns the function
f(x) == y. For a detailed description of the algorithm read the above paper.
</p>
<p>
Note that if you want to use the linear kernel then you would
be better off using the <a href="#rls">rls</a> object as it
is optimized for this case.
</p>
</description>
<examples>
<example>krls_ex.cpp.html</example>
<example>krls_filter_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>rls</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/rls_abstract.h</spec_file>
<description>
This is an implementation of the linear version of the recursive least
squares algorithm. It accepts training points incrementally and, at
each step, maintains the solution to the following optimization problem:
<blockquote>
find w minimizing: 0.5*dot(w,w) + C*sum_i(y_i - trans(x_i)*w)^2
</blockquote>
Where (x_i,y_i) are training pairs. x_i is some vector and y_i is a target
scalar value.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>svm_pegasos</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/pegasos_abstract.h</spec_file>
<description>
This object implements an online algorithm for training a support
vector machine for solving binary classification problems.
<p>
The implementation of the Pegasos algorithm used by this object is based
on the following excellent paper:
<blockquote>
Pegasos: Primal estimated sub-gradient solver for SVM (2007)
by Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro
In ICML
</blockquote>
</p>
<p>
This SVM training algorithm has two interesting properties. First, the
pegasos algorithm itself converges to the solution in an amount of time
unrelated to the size of the training set (in addition to being quite fast
to begin with). This makes it an appropriate algorithm for learning from
very large datasets. Second, this object uses the <a href="#kcentroid">kcentroid</a> object
to maintain a sparse approximation of the learned decision function.
This means that the number of support vectors in the resulting decision
function is also unrelated to the size of the dataset (in normal SVM
training algorithms, the number of support vectors grows approximately
linearly with the size of the training set).
</p>
<p>
However, if you are considering using svm_pegasos, you should also try the
<a href="#svm_c_linear_trainer">svm_c_linear_trainer</a> for linear
kernels or <a href="#svm_c_ekm_trainer">svm_c_ekm_trainer</a> for non-linear
kernels since these other trainers are, usually, faster and easier to use
than svm_pegasos.
</p>
</description>
<examples>
<example>svm_pegasos_ex.cpp.html</example>
<example>svm_sparse_ex.cpp.html</example>
<example>svm_binary_classifier.py.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>kkmeans</name>
<file>dlib/clustering.h</file>
<spec_file link="true">dlib/svm/kkmeans_abstract.h</spec_file>
<description>
This is an implementation of a kernelized k-means clustering algorithm.
It performs k-means clustering by using the <a href="#kcentroid">kcentroid</a> object.
<p>
If you want to use the linear kernel (i.e. do a normal k-means clustering) then you
should use the <a href="#find_clusters_using_kmeans">find_clusters_using_kmeans</a> routine.
</p>
</description>
<examples>
<example>kkmeans_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>vector_normalizer</name>
<file>dlib/statistics.h</file>
<spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file>
<description>
This object represents something that can learn to normalize a set
of column vectors. In particular, normalized column vectors should
have zero mean and a variance of one.
</description>
<examples>
<example>svm_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>vector_normalizer_frobmetric</name>
<file>dlib/statistics.h</file>
<spec_file link="true">dlib/statistics/vector_normalizer_frobmetric_abstract.h</spec_file>
<description>
This object is a tool for performing the FrobMetric distance metric
learning algorithm described in the following paper:
<blockquote>
A Scalable Dual Approach to Semidefinite Metric Learning
By Chunhua Shen, Junae Kim, Lei Wang, in CVPR 2011
</blockquote>
Therefore, this object is a tool that takes as input training triplets
(anchor, near, far) of vectors and attempts to learn a linear
transformation T such that:
<blockquote> <tt>length(T*anchor-T*near) + 1 < length(T*anchor - T*far)</tt> </blockquote>
That is, you give a bunch of anchor vectors and for each anchor vector you
specify some vectors which should be near to it and some that should be far
form it. This object then tries to find a transformation matrix that makes
the "near" vectors close to their anchors while the "far" vectors are
farther away.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>compute_lda_transform</name>
<file>dlib/statistics.h</file>
<spec_file link="true">dlib/statistics/lda_abstract.h</spec_file>
<description>
This function performs the dimensionality reducing version of linear
discriminant analysis. That is, you give it a set of labeled vectors and it
returns a linear transform that maps the input vectors into a new space that
is good for distinguishing between the different classes.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>discriminant_pca</name>
<file>dlib/statistics.h</file>
<spec_file link="true">dlib/statistics/dpca_abstract.h</spec_file>
<description>
This object implements the Discriminant PCA technique described in the paper:
<blockquote>
A New Discriminant Principal Component Analysis Method with Partial Supervision (2009)
by Dan Sun and Daoqiang Zhang
</blockquote>
This algorithm is basically a straightforward generalization of the classical PCA
technique to handle partially labeled data. It is useful if you want to learn a linear
dimensionality reduction rule using a bunch of data that is partially labeled.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>sammon_projection</name>
<file>dlib/statistics.h</file>
<spec_file link="true">dlib/statistics/sammon_abstract.h</spec_file>
<description>
This is a function object that computes the Sammon projection of a set
of N points in a L-dimensional vector space onto a d-dimensional space
(d < L), according to the paper:
<blockquote>
A Nonlinear Mapping for Data Structure Analysis (1969) by J.W. Sammon
</blockquote>
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>cca</name>
<file>dlib/statistics.h</file>
<spec_file link="true">dlib/statistics/cca_abstract.h</spec_file>
<description>
This function performs a canonical correlation analysis between two sets
of vectors. Additionally, it is designed to be very fast, even for large
datasets of over a million high dimensional vectors.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>vector_normalizer_pca</name>
<file>dlib/statistics.h</file>
<spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file>
<description>
This object represents something that can learn to normalize a set
of column vectors. In particular, normalized column vectors should
have zero mean and a variance of one.
This object also uses principal component analysis for the purposes
of reducing the number of elements in a vector.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>linearly_independent_subset_finder</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/linearly_independent_subset_finder_abstract.h</spec_file>
<description>
<p>
This is an implementation of an online algorithm for recursively finding a
set (aka dictionary) of linearly independent vectors in a kernel induced
feature space. To use it you decide how large you would like the dictionary
to be and then you feed it sample points.
</p>
<p>
The implementation uses the Approximately Linearly Dependent metric described
in the paper The Kernel Recursive Least Squares Algorithm by Yaakov Engel to
decide which points are more linearly independent than others. The metric is
simply the squared distance between a test point and the subspace spanned by
the set of dictionary vectors.
</p>
<p>
Each time you present this object with a new sample point
it calculates the projection distance and if it is sufficiently large then this
new point is included into the dictionary. Note that this object can be configured
to have a maximum size. Once the max dictionary size is reached each new point
kicks out a previous point. This is done by removing the dictionary vector that
has the smallest projection distance onto the others. That is, the "least linearly
independent" vector is removed to make room for the new one.
</p>
</description>
<examples>
<example>empirical_kernel_map_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>fill_lisf</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/linearly_independent_subset_finder_abstract.h</spec_file>
<description>
This is a simple function for filling a
<a href="#linearly_independent_subset_finder">linearly_independent_subset_finder</a>
with data points by using random sampling.
</description>
<examples>
<example>empirical_kernel_map_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>sort_basis_vectors</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/sort_basis_vectors_abstract.h</spec_file>
<description>
A kernel based learning method ultimately needs to select a set of basis functions
represented by a particular choice of kernel and a set of basis vectors.
sort_basis_vectors() is a function which attempts to perform supervised
basis set selection. In particular, you give it a candidate set of basis
vectors and it sorts them according to how useful they are for solving
a particular decision problem.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>rank_unlabeled_training_samples</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/active_learning_abstract.h</spec_file>
<description>
This routine implements an active learning method for selecting the most
informative data sample to label out of a set of unlabeled samples.
In particular, it implements the MaxMin Margin and Ratio Margin methods
described in the paper:
<blockquote>
Support Vector Machine Active Learning with Applications to Text Classification
by Simon Tong and Daphne Koller.
</blockquote>
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>linear_manifold_regularizer</name>
<file>dlib/manifold_regularization.h</file>
<spec_file link="true">dlib/manifold_regularization/linear_manifold_regularizer_abstract.h</spec_file>
<description>
<p>
Many learning algorithms attempt to minimize a function that, at a high
level, looks like this:
<pre>
f(w) == complexity + training_set_error
</pre>
</p>
<p>
The idea is to find the set of parameters, w, that gives low error on
your training data but also is not "complex" according to some particular
measure of complexity. This strategy of penalizing complexity is
usually called regularization.
</p>
<p>
In the above setting, all the training data consists of labeled samples.
However, it would be nice to be able to benefit from unlabeled data.
The idea of manifold regularization is to extract useful information from
unlabeled data by first defining which data samples are "close" to each other
(perhaps by using their 3 <a href="graph_tools.html#find_k_nearest_neighbors">nearest neighbors</a>)
and then adding a term to
the above function that penalizes any decision rule which produces
different outputs on data samples which we have designated as being close.
</p>
<p>
It turns out that it is possible to transform these manifold regularized learning
problems into the normal form shown above by applying a certain kind of
preprocessing to all our data samples. Once this is done we can use a
normal learning algorithm, such as the <a href="#svm_c_linear_trainer">svm_c_linear_trainer</a>,
on just the
labeled data samples and obtain the same output as the manifold regularized
learner would have produced.
</p>
<p>
The linear_manifold_regularizer is a tool for creating this preprocessing
transformation. In particular, the transformation is linear. That is, it
is just a matrix you multiply with all your samples. For a more detailed
discussion of this topic you should consult the following paper. In
particular, see section 4.2. This object computes the inverse T matrix
described in that section.
<blockquote>
Linear Manifold Regularization for Large Scale Semi-supervised Learning
by Vikas Sindhwani, Partha Niyogi, and Mikhail Belkin
</blockquote>
</p>
</description>
<examples>
<example>linear_manifold_regularizer_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>empirical_kernel_map</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/empirical_kernel_map_abstract.h</spec_file>
<description>
<p>
This object represents a map from objects of sample_type (the kind of object
a <a href="dlib/svm/kernel_abstract.h.html#Kernel_Function_Objects">kernel function</a>
operates on) to finite dimensional column vectors which
represent points in the kernel feature space defined by whatever kernel
is used with this object.
</p>
<p>
To use the empirical_kernel_map you supply it with a particular kernel and a set of
basis samples. After that you can present it with new samples and it will project
them into the part of kernel feature space spanned by your basis samples.
</p>
<p>
This means the empirical_kernel_map is a tool you can use to very easily kernelize
any algorithm that operates on column vectors. All you have to do is select a
set of basis samples and then use the empirical_kernel_map to project all your
data points into the part of kernel feature space spanned by those basis samples.
Then just run your normal algorithm on the output vectors and it will be effectively
kernelized.
</p>
<p>
Regarding methods to select a set of basis samples, if you are working with only a
few thousand samples then you can just use all of them as basis samples.
Alternatively, the
<a href="#linearly_independent_subset_finder">linearly_independent_subset_finder</a>
often works well for selecting a basis set. I also find that picking a
<a href="algorithms.html#random_subset_selector">random subset</a> typically works well.
</p>
</description>
<examples>
<example>empirical_kernel_map_ex.cpp.html</example>
<example>linear_manifold_regularizer_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>kcentroid</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/kcentroid_abstract.h</spec_file>
<description>
This object represents a weighted sum of sample points in a kernel induced
feature space. It can be used to kernelize any algorithm that requires only
the ability to perform vector addition, subtraction, scalar multiplication,
and inner products.
<p>
An example use of this object is as an online algorithm for recursively estimating
the centroid of a sequence of training points. This object then allows you to
compute the distance between the centroid and any test points. So you can use
this object to predict how similar a test point is to the data this object has
been trained on (larger distances from the centroid indicate dissimilarity/anomalous
points).
</p>
<p>
The object internally keeps a set of "dictionary vectors"
that are used to represent the centroid. It manages these vectors using the
sparsification technique described in the paper The Kernel Recursive Least
Squares Algorithm by Yaakov Engel. This technique allows us to keep the
number of dictionary vectors down to a minimum. In fact, the object has a
user selectable tolerance parameter that controls the trade off between
accuracy and number of stored dictionary vectors.
</p>
</description>
<examples>
<example>kcentroid_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>train_probabilistic_decision_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
<description>
<p>
Trains a <a href="#probabilistic_function">probabilistic_function</a> using
some sort of binary classification trainer object such as the <a href="#svm_nu_trainer">svm_nu_trainer</a> or
<a href="#krr_trainer">krr_trainer</a>.
</p>
The probability model is created by using the technique described in the following papers:
<blockquote>
Probabilistic Outputs for Support Vector Machines and
Comparisons to Regularized Likelihood Methods by
John C. Platt. March 26, 1999
</blockquote>
<blockquote>
A Note on Platt's Probabilistic Outputs for Support Vector Machines
by Hsuan-Tien Lin, Chih-Jen Lin, and Ruby C. Weng
</blockquote>
</description>
<examples>
<example>svm_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>learn_platt_scaling</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
<description>
<p>
This function is an implementation of the algorithm described in the following
papers:
<blockquote>
Probabilistic Outputs for Support Vector Machines and Comparisons to
Regularized Likelihood Methods by John C. Platt. March 26, 1999
<br/>
<br/>
A Note on Platt's Probabilistic Outputs for Support Vector Machines
by Hsuan-Tien Lin, Chih-Jen Lin, and Ruby C. Weng
</blockquote>
</p>
<p>
This function is the tool used to implement the
<a href="#train_probabilistic_decision_function">train_probabilistic_decision_function</a> routine.
</p>
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>probabilistic</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
<description>
This is a trainer adapter which simply runs the trainer it is given though the
<a href="#train_probabilistic_decision_function">train_probabilistic_decision_function</a>
function.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>rbf_network_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/rbf_network_abstract.h</spec_file>
<description>
Trains a radial basis function network and outputs a <a href="#decision_function">decision_function</a>.
This object can be used for either regression or binary classification problems.
It's worth pointing out that this object is essentially an unregularized version
of <a href="#krr_trainer">kernel ridge regression</a>. This means
you should really prefer to use kernel ridge regression instead.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>random_forest_regression_trainer</name>
<file>dlib/random_forest.h</file>
<spec_file link="true">dlib/random_forest/random_forest_regression_abstract.h</spec_file>
<description>
This object implements Breiman's classic random forest regression
algorithm.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>random_forest_regression_function</name>
<file>dlib/random_forest.h</file>
<spec_file link="true">dlib/random_forest/random_forest_regression_abstract.h</spec_file>
<description>
This object represents a random forest that maps objects to real numbers. You
can learn its parameters using the <a href="#random_forest_regression_trainer">random_forest_regression_trainer</a>.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>rvm_regression_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/rvm_abstract.h</spec_file>
<description>
<p>
Trains a relevance vector machine for solving regression problems.
Outputs a <a href="#decision_function">decision_function</a> that represents the learned
regression function.
</p>
The implementation of the RVM training algorithm used by this library is based
on the following paper:
<blockquote>
Tipping, M. E. and A. C. Faul (2003). Fast marginal likelihood maximisation
for sparse Bayesian models. In C. M. Bishop and B. J. Frey (Eds.), Proceedings
of the Ninth International Workshop on Artificial Intelligence and Statistics,
Key West, FL, Jan 3-6.
</blockquote>
</description>
<examples>
<example>rvm_regression_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>rvm_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/rvm_abstract.h</spec_file>
<description>
<p>
Trains a relevance vector machine for solving binary classification problems.
Outputs a <a href="#decision_function">decision_function</a> that represents the learned classifier.
</p>
The implementation of the RVM training algorithm used by this library is based
on the following paper:
<blockquote>
Tipping, M. E. and A. C. Faul (2003). Fast marginal likelihood maximisation
for sparse Bayesian models. In C. M. Bishop and B. J. Frey (Eds.), Proceedings
of the Ninth International Workshop on Artificial Intelligence and Statistics,
Key West, FL, Jan 3-6.
</blockquote>
</description>
<examples>
<example>rvm_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>krr_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/krr_trainer_abstract.h</spec_file>
<description>
<p>
Performs kernel ridge regression and outputs a <a href="#decision_function">decision_function</a> that
represents the learned function.
</p>
The implementation is done using the <a href="#empirical_kernel_map">empirical_kernel_map</a> and
<a href="#linearly_independent_subset_finder">linearly_independent_subset_finder</a> to kernelize
the <a href="#rr_trainer">rr_trainer</a> object. Thus it allows you to run the algorithm on large
datasets and obtain sparse outputs. It is also capable of automatically estimating its
regularization parameter using leave-one-out cross-validation.
</description>
<examples>
<example>krr_regression_ex.cpp.html</example>
<example>krr_classification_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>rr_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/rr_trainer_abstract.h</spec_file>
<description>
<p>
Performs linear ridge regression and outputs a <a href="#decision_function">decision_function</a> that
represents the learned function. In particular, this object can only be used with
the <a href="#linear_kernel">linear_kernel</a>. It is optimized for the linear case where
the number of features in each sample vector is small (i.e. on the order of 1000 or less since the
algorithm is cubic in the number of features.).
If you want to use a nonlinear kernel then you should use the <a href="#krr_trainer">krr_trainer</a>.
</p>
This object is capable of automatically estimating its regularization parameter using
leave-one-out cross-validation.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>svr_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svr_trainer_abstract.h</spec_file>
<description>
<p>
This object implements a trainer for performing epsilon-insensitive support
vector regression. It is implemented using the <a href="optimization.html#solve_qp3_using_smo">SMO</a> algorithm,
allowing the use of non-linear kernels.
If you are interested in performing support vector regression with a linear kernel and you
have a lot of training data then you should use the <a href="#svr_linear_trainer">svr_linear_trainer</a>
which is highly optimized for this case.
</p>
The implementation of the eps-SVR training algorithm used by this object is based
on the following paper:
<ul>
<li>Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector
machines, 2001. Software available at
<a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm">http://www.csie.ntu.edu.tw/~cjlin/libsvm</a></li>
</ul>
</description>
<examples>
<example>svr_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>svr_linear_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svr_linear_trainer_abstract.h</spec_file>
<description>
This object implements a trainer for performing epsilon-insensitive support
vector regression. It uses the <a href="optimization.html#oca">oca</a>
optimizer so it is very efficient at solving this problem when
linear kernels are used, making it suitable for use with large
datasets.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>svm_nu_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_nu_trainer_abstract.h</spec_file>
<description>
<p>
Trains a nu support vector machine for solving binary classification problems and
outputs a <a href="#decision_function">decision_function</a>.
It is implemented using the <a href="optimization.html#solve_qp2_using_smo">SMO</a> algorithm.
</p>
The implementation of the nu-svm training algorithm used by this library is based
on the following excellent papers:
<ul>
<li>Chang and Lin, Training {nu}-Support Vector Classifiers: Theory and Algorithms</li>
<li>Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector
machines, 2001. Software available at
<a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm">http://www.csie.ntu.edu.tw/~cjlin/libsvm</a></li>
</ul>
</description>
<examples>
<example>svm_ex.cpp.html</example>
<example>model_selection_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>svm_one_class_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_one_class_trainer_abstract.h</spec_file>
<description>
<p>
Trains a one-class support vector classifier and outputs a <a href="#decision_function">decision_function</a>.
It is implemented using the <a href="optimization.html#solve_qp3_using_smo">SMO</a> algorithm.
</p>
The implementation of the one-class training algorithm used by this library is based
on the following paper:
<ul>
<li>Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector
machines, 2001. Software available at
<a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm">http://www.csie.ntu.edu.tw/~cjlin/libsvm</a></li>
</ul>
</description>
<examples>
<example>one_class_classifiers_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>svm_c_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_c_trainer_abstract.h</spec_file>
<description>
<p>
Trains a C support vector machine for solving binary classification problems
and outputs a <a href="#decision_function">decision_function</a>.
It is implemented using the <a href="optimization.html#solve_qp3_using_smo">SMO</a> algorithm.
</p>
The implementation of the C-SVM training algorithm used by this library is based
on the following paper:
<ul>
<li>Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector
machines, 2001. Software available at
<a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm">http://www.csie.ntu.edu.tw/~cjlin/libsvm</a></li>
</ul>
</description>
<examples>
<example>svm_c_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>svm_c_linear_dcd_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_c_linear_dcd_trainer_abstract.h</spec_file>
<description>
This object represents a tool for training the C formulation of
a support vector machine to solve binary classification problems.
It is optimized for the case where linear kernels are used and
is implemented using the method described in the
following paper:
<blockquote>
A Dual Coordinate Descent Method for Large-scale Linear SVM
by Cho-Jui Hsieh, Kai-Wei Chang, and Chih-Jen Lin
</blockquote>
This trainer has the ability to disable the bias term and also
to force the last element of the learned weight vector to be 1.
Additionally, it can be warm-started from the solution to a previous
training run.
</description>
<examples>
<example>one_class_classifiers_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>svm_c_linear_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_c_linear_trainer_abstract.h</spec_file>
<description>
This object represents a tool for training the C formulation of
a support vector machine to solve binary classification problems.
It is optimized for the case where linear kernels are used and
is implemented using the <a href="optimization.html#oca">oca</a>
optimizer and uses the exact line search described in the
following paper:
<blockquote>
Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization
by Vojtech Franc, Soren Sonnenburg; Journal of Machine Learning
Research, 10(Oct):2157--2192, 2009.
</blockquote>
This trainer has the ability to restrict the learned weights to non-negative
values.
</description>
<examples>
<example>svm_sparse_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>svm_rank_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_rank_trainer_abstract.h</spec_file>
<description>
This object represents a tool for training a ranking support vector machine
using linear kernels. In particular, this object is a tool for training
the Ranking SVM described in the paper:
<blockquote>
Optimizing Search Engines using Clickthrough Data by Thorsten Joachims
</blockquote>
Finally, note that the implementation of this object is done using the
<a href="optimization.html#oca">oca</a> optimizer and
<a href="#count_ranking_inversions">count_ranking_inversions</a> method.
This means that it runs in O(n*log(n)) time, making it suitable for use
with large datasets.
</description>
<examples>
<example>svm_rank_ex.cpp.html</example>
<example>svm_rank.py.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>shape_predictor_trainer</name>
<file>dlib/image_processing.h</file>
<spec_file link="true">dlib/image_processing/shape_predictor_trainer_abstract.h</spec_file>
<description>
This object is a tool for training <a href="imaging.html#shape_predictor">shape_predictors</a>
based on annotated training images. Its implementation uses the algorithm described in:
<blockquote>
One Millisecond Face Alignment with an Ensemble of Regression Trees
by Vahid Kazemi and Josephine Sullivan, CVPR 2014
</blockquote>
It is capable of learning high quality shape models. For example, this is an example output
for one of the faces in the HELEN face dataset: <br/><br/>
<img src='face_landmarking_example.png'/>
</description>
<examples>
<example>train_shape_predictor_ex.cpp.html</example>
<example>train_shape_predictor.py.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>svm_c_ekm_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_c_ekm_trainer_abstract.h</spec_file>
<description>
This object represents a tool for training the C formulation of
a support vector machine for solving binary classification problems.
It is implemented using the <a href="#empirical_kernel_map">empirical_kernel_map</a>
to kernelize the <a href="#svm_c_linear_trainer">svm_c_linear_trainer</a>. This makes it a very fast algorithm
capable of learning from very large datasets.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>normalized_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/function_abstract.h</spec_file>
<description>
This object represents a container for another function
object and an instance of the <a href="#vector_normalizer">vector_normalizer</a> object.
It automatically normalizes all inputs before passing them
off to the contained function object.
</description>
<examples>
<example>svm_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>probabilistic_decision_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/function_abstract.h</spec_file>
<description>
This object represents a binary decision function for use with
kernel-based learning-machines. It returns an
estimate of the probability that a given sample is in the +1 class.
</description>
<examples>
<example>svm_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>probabilistic_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/function_abstract.h</spec_file>
<description>
This object represents a binary decision function for use with
any kind of binary classifier. It returns an
estimate of the probability that a given sample is in the +1 class.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>distance_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/function_abstract.h</spec_file>
<description>
This object represents a point in kernel induced feature space.
You may use this object to find the distance from the point it
represents to points in input space as well as other points
represented by distance_functions.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>decision_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/function_abstract.h</spec_file>
<description>
This object represents a classification or regression function that was
learned by a kernel based learning algorithm. Therefore, it is a function
object that takes a sample object and returns a scalar value.
</description>
<examples>
<example>svm_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>one_vs_one_decision_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/one_vs_one_decision_function_abstract.h</spec_file>
<description>
This object represents a multiclass classifier built out
of a set of binary classifiers. Each binary classifier
is used to vote for the correct multiclass label using a
one vs. one strategy. Therefore, if you have N classes then
there will be N*(N-1)/2 binary classifiers inside this object.
</description>
<examples>
<example>multiclass_classification_ex.cpp.html</example>
<example>custom_trainer_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>one_vs_one_trainer</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/one_vs_one_trainer_abstract.h</spec_file>
<description>
This object is a tool for turning a bunch of binary classifiers
into a multiclass classifier. It does this by training the binary
classifiers in a one vs. one fashion. That is, if you have N possible
classes then it trains N*(N-1)/2 binary classifiers which are then used
to vote on the identity of a test sample.
</description>
<examples>
<example>multiclass_classification_ex.cpp.html</example>
<example>custom_trainer_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>one_vs_all_decision_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/one_vs_all_decision_function_abstract.h</spec_file>
<description>
This object represents a multiclass classifier built out
of a set of binary classifiers. Each binary classifier
is used to vote for the correct multiclass label using a
one vs. all strategy. Therefore, if you have N classes then
there will be N binary classifiers inside this object.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>sequence_labeler</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/sequence_labeler_abstract.h</spec_file>
<description>
This object is a tool for doing sequence labeling. In particular,
it is capable of representing sequence labeling models such as
those produced by Hidden Markov SVMs or Conditional Random fields.
See the following papers for an introduction to these techniques:
<blockquote>
Hidden Markov Support Vector Machines by
Y. Altun, I. Tsochantaridis, T. Hofmann
<br/>
Shallow Parsing with Conditional Random Fields by
Fei Sha and Fernando Pereira
</blockquote>
</description>
<examples>
<example>sequence_labeler_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>sequence_segmenter</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/sequence_segmenter_abstract.h</spec_file>
<description>
This object is a tool for segmenting a sequence of objects into a set of
non-overlapping chunks. An example sequence segmentation task is to take
English sentences and identify all the named entities. In this example,
you would be using a sequence_segmenter to find all the chunks of
contiguous words which refer to proper names.
<p>
Internally, the sequence_segmenter uses the BIO (Begin, Inside, Outside) or
BILOU (Begin, Inside, Last, Outside, Unit) sequence tagging model.
Moreover, it is implemented using a <a href="#sequence_labeler">sequence_labeler</a>
object and therefore sequence_segmenter objects are examples of
chain structured conditional random field style sequence
taggers.
</p>
</description>
<examples>
<example>sequence_segmenter.py.html</example>
<example>sequence_segmenter_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>assignment_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/assignment_function_abstract.h</spec_file>
<description>
This object is a tool for solving the optimal assignment problem given a
user defined method for computing the quality of any particular assignment.
</description>
<examples>
<example>assignment_learning_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>track_association_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/track_association_function_abstract.h</spec_file>
<description>
This object is a tool that helps you implement an object tracker. So for
example, if you wanted to track people moving around in a video then this
object can help. In particular, imagine you have a tool for detecting the
positions of each person in an image. Then you can run this person
detector on the video and at each time step, i.e. at each frame, you get a
set of person detections. However, that by itself doesn't tell you how
many people there are in the video and where they are moving to and from.
To get that information you need to figure out which detections match each
other from frame to frame. This is where the track_association_function
comes in. It performs the detection to track association. It will also do
some of the track management tasks like creating a new track when a
detection doesn't match any of the existing tracks.
<p>
Internally, this object is implemented using the
<a href="#assignment_function">assignment_function</a> object.
In fact, it's really just a thin wrapper around assignment_function and
exists just to provide a more convenient interface to users doing detection
to track association.
</p>
</description>
<examples>
<example>learning_to_track_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>lspi</name>
<file>dlib/control.h</file>
<spec_file link="true">dlib/control/lspi_abstract.h</spec_file>
<description>
This object is an implementation of the reinforcement learning algorithm
described in the following paper:
<blockquote>
Lagoudakis, Michail G., and Ronald Parr. "Least-squares policy
iteration." The Journal of Machine Learning Research 4 (2003):
1107-1149.
</blockquote>
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>policy</name>
<file>dlib/control.h</file>
<spec_file link="true">dlib/control/approximate_linear_models_abstract.h</spec_file>
<description>
This is a policy (i.e. a control law) based on a linear function approximator.
You can use a tool like <a href="#lspi">lspi</a> to learn the parameters
of a policy.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>process_sample</name>
<file>dlib/control.h</file>
<spec_file link="true">dlib/control/approximate_linear_models_abstract.h</spec_file>
<description>
This object holds a training sample for a reinforcement learning algorithm
(e.g. <a href="#lspi">lspi</a>).
In particular, it contains a state, action, reward, next state sample from
some process.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>graph_labeler</name>
<file>dlib/graph_cuts.h</file>
<spec_file link="true">dlib/graph_cuts/graph_labeler_abstract.h</spec_file>
<description>
This object is a tool for labeling each node in a <a href="containers.html#graph">graph</a>
with a value of true or false, subject to a labeling consistency constraint between
nodes that share an edge. In particular, this object is useful for
representing a graph labeling model learned via some machine learning
method, such as the <a href="#structural_graph_labeling_trainer">structural_graph_labeling_trainer</a>.
</description>
<examples>
<example>graph_labeling_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>multiclass_linear_decision_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/function_abstract.h</spec_file>
<description>
This object represents a multiclass classifier built out of a set of
binary classifiers. Each binary classifier is used to vote for the
correct multiclass label using a one vs. all strategy. Therefore,
if you have N classes then there will be N binary classifiers inside
this object. Additionally, this object is linear in the sense that
each of these binary classifiers is a simple linear plane.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>one_vs_all_trainer</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/one_vs_all_trainer_abstract.h</spec_file>
<description>
This object is a tool for turning a bunch of binary classifiers
into a multiclass classifier. It does this by training the binary
classifiers in a one vs. all fashion. That is, if you have N possible
classes then it trains N binary classifiers which are then used
to vote on the identity of a test sample.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>svm_multiclass_linear_trainer</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/svm_multiclass_linear_trainer_abstract.h</spec_file>
<description>
This object represents a tool for training a multiclass support
vector machine. It is optimized for the case where linear kernels
are used and implemented using the <a href="#structural_svm_problem">structural_svm_problem</a>
object.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>projection_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/function_abstract.h</spec_file>
<description>
This object represents a function that takes a data sample and projects
it into kernel feature space. The result is a real valued column vector that
represents a point in a kernel feature space. Instances of
this object are created using the
<a href="#empirical_kernel_map">empirical_kernel_map</a>.
</description>
<examples>
<example>linear_manifold_regularizer_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>offset_kernel</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/kernel_abstract.h</spec_file>
<description>
This object represents a kernel with a fixed value offset
added to it.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>linear_kernel</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/kernel_abstract.h</spec_file>
<description>
This object represents a linear function kernel for use with
kernel learning machines.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>histogram_intersection_kernel</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/kernel_abstract.h</spec_file>
<description>
This object represents a histogram intersection kernel for use with
kernel learning machines.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>sigmoid_kernel</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/kernel_abstract.h</spec_file>
<description>
This object represents a sigmoid kernel for use with
kernel learning machines.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>polynomial_kernel</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/kernel_abstract.h</spec_file>
<description>
This object represents a polynomial kernel for use with
kernel learning machines.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>radial_basis_kernel</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/kernel_abstract.h</spec_file>
<description>
This object represents a radial basis function kernel for use with
kernel learning machines.
</description>
<examples>
<example>svm_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>sparse_histogram_intersection_kernel</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/sparse_kernel_abstract.h</spec_file>
<description>
This object represents a histogram intersection kernel kernel for use with
kernel learning machines that operate on
<a href="dlib/svm/sparse_vector_abstract.h.html#sparse_vectors">sparse vectors</a>.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>sparse_sigmoid_kernel</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/sparse_kernel_abstract.h</spec_file>
<description>
This object represents a sigmoid kernel for use with
kernel learning machines that operate on
<a href="dlib/svm/sparse_vector_abstract.h.html#sparse_vectors">sparse vectors</a>.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>sparse_linear_kernel</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/sparse_kernel_abstract.h</spec_file>
<description>
This object represents a linear kernel for use with
kernel learning machines that operate on
<a href="dlib/svm/sparse_vector_abstract.h.html#sparse_vectors">sparse vectors</a>.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>sparse_polynomial_kernel</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/sparse_kernel_abstract.h</spec_file>
<description>
This object represents a polynomial kernel for use with
kernel learning machines that operate on
<a href="dlib/svm/sparse_vector_abstract.h.html#sparse_vectors">sparse vectors</a>.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>sparse_radial_basis_kernel</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/sparse_kernel_abstract.h</spec_file>
<description>
This object represents a radial basis function kernel for use with
kernel learning machines that operate on
<a href="dlib/svm/sparse_vector_abstract.h.html#sparse_vectors">sparse vectors</a>.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>is_binary_classification_problem</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
<description>
This function simply takes two vectors, the first containing feature vectors and
the second containing labels, and reports back if the two could possibly
contain data for a well formed classification problem.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>is_sequence_labeling_problem</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
<description>
This function takes a set of training data for a sequence labeling problem
and reports back if it could possibly be a well formed sequence labeling problem.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>is_sequence_segmentation_problem</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
<description>
This function takes a set of training data for a sequence segmentation problem
and reports back if it could possibly be a well formed sequence segmentation problem.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>is_assignment_problem</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
<description>
This function takes a set of training data for an assignment problem
and reports back if it could possibly be a well formed assignment problem.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>is_track_association_problem</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
<description>
This function takes a set of training data for a track association learning problem
and reports back if it could possibly be a well formed track association problem.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>is_graph_labeling_problem</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/structural_svm_graph_labeling_problem_abstract.h</spec_file>
<description>
This function takes a set of training data for a graph labeling problem
and reports back if it could possibly be a well formed problem.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>is_forced_assignment_problem</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
<description>
This function takes a set of training data for a forced assignment problem
and reports back if it could possibly be a well formed forced assignment problem.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>is_learning_problem</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
<description>
This function simply takes two vectors, the first containing feature vectors and
the second containing labels, and reports back if the two could possibly
contain data for a well formed learning problem. In this case it just means
that the two vectors have the same length and aren't empty.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>select_all_distinct_labels</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/multiclass_tools_abstract.h</spec_file>
<description>
This is a function which determines all distinct values present in a
std::vector and returns the result.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>simplify_linear_decision_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/simplify_linear_decision_function_abstract.h</spec_file>
<description>
This is a set of functions that takes various forms of linear <a href="#decision_function">decision functions</a>
and collapses them down so that they only compute a single dot product when invoked.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>randomize_samples</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
<description>
Randomizes the order of samples in a column vector containing sample data.
</description>
<examples>
<example>svm_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>rank_features</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/feature_ranking_abstract.h</spec_file>
<description>
Finds a ranking of the top N (a user supplied parameter) features in a set of data
from a two class classification problem. It
does this by computing the distance between the centroids of both classes in kernel defined
feature space. Good features are then ones that result in the biggest separation between
the two centroids.
</description>
<examples>
<example>rank_features_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>load_mnist_dataset</name>
<file>dlib/data_io.h</file>
<spec_file>dlib/data_io/mnist_abstract.h</spec_file>
<description>
Loads the <a href="http://yann.lecun.com/exdb/mnist/">MNIST dataset</a> from disk.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>load_image_dataset</name>
<file>dlib/data_io.h</file>
<spec_file link="true">dlib/data_io/load_image_dataset_abstract.h</spec_file>
<description>
This is a function which loads the list of images indicated by an
<a href="#load_image_dataset_metadata">image dataset metadata file</a>
as well as the box locations for each image. It makes loading the
data necessary to train an <a href="imaging.html#object_detector">object_detector</a>
a little more convenient.
</description>
<examples>
<example>fhog_object_detector_ex.cpp.html</example>
<example>train_object_detector.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>load_image_dataset_metadata</name>
<file>dlib/data_io.h</file>
<spec_file link="true">dlib/data_io/image_dataset_metadata.h</spec_file>
<description>
dlib comes with a graphical tool for annotating images with
labeled rectangles. The tool produces an XML file containing these
annotations. Therefore, load_image_dataset_metadata() is a routine
for parsing these XML files. Note also that this is the metadata
format used by the image labeling tool included with dlib in the
tools/imglab folder.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>save_image_dataset_metadata</name>
<file>dlib/data_io.h</file>
<spec_file link="true">dlib/data_io/image_dataset_metadata.h</spec_file>
<description>
This routine is a tool for saving labeled image metadata to an
XML file. In particular, this routine saves the metadata into a
form which can be read by the <a href="#load_image_dataset_metadata">load_image_dataset_metadata</a>
routine. Note also that this is the metadata
format used by the image labeling tool included with dlib in the
tools/imglab folder.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>load_libsvm_formatted_data</name>
<file>dlib/data_io.h</file>
<spec_file link="true">dlib/data_io/libsvm_io_abstract.h</spec_file>
<description>
This is a function that loads the data from a file that uses
the LIBSVM format. It loads the data into a std::vector of
<a href="dlib/svm/sparse_vector_abstract.h.html#sparse_vectors">sparse vectors</a>.
If you want to load data into dense vectors (i.e.
dlib::matrix objects) then you can use the <a href="linear_algebra.html#sparse_to_dense">sparse_to_dense</a>
function to perform the conversion. Also, some LIBSVM formatted files number
their features beginning with 1 rather than 0. If this bothers you, then you
can fix it by using the <a href="#fix_nonzero_indexing">fix_nonzero_indexing</a> function
on the data after it is loaded.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>save_libsvm_formatted_data</name>
<file>dlib/data_io.h</file>
<spec_file link="true">dlib/data_io/libsvm_io_abstract.h</spec_file>
<description>
This is actually a pair of overloaded functions. Between the two of them
they let you save <a href="dlib/svm/sparse_vector_abstract.h.html#sparse_vectors">sparse</a>
or dense data vectors to file using the LIBSVM format.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>make_bounding_box_regression_training_data</name>
<file>dlib/image_processing.h</file>
<spec_file link="true">dlib/image_processing/shape_predictor_trainer_abstract.h</spec_file>
<description>
Suppose you have an object detector that can roughly locate objects in an
image. This means your detector draws boxes around objects, but these are
<i>rough</i> boxes in the sense that they aren't positioned super accurately. For
instance, HOG based detectors usually have a stride of 8 pixels. So the
positional accuracy is going to be, at best, +/-8 pixels.
<p>
If you want to get better positional accuracy one easy thing to do is train a
<a href="#shape_predictor_trainer">shape_predictor</a> to give you the location
of the object's box. The make_bounding_box_regression_training_data() routine
helps you do this by creating an appropriate training dataset.
</p>
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>fix_nonzero_indexing</name>
<file>dlib/data_io.h</file>
<spec_file link="true">dlib/data_io/libsvm_io_abstract.h</spec_file>
<description>
This is a simple function that takes a std::vector of
<a href="dlib/svm/sparse_vector_abstract.h.html#sparse_vectors">sparse vectors</a>
and makes sure they are zero-indexed (e.g. makes sure the first index value is zero).
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>find_gamma_with_big_centroid_gap</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/feature_ranking_abstract.h</spec_file>
<description>
This is a function that tries to pick a reasonable default value for the
gamma parameter of the <a href="#radial_basis_kernel">radial_basis_kernel</a>. It
picks the parameter that gives the largest separation between the centroids, in
kernel feature space, of two classes of data.
</description>
<examples>
<example>rank_features_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>compute_mean_squared_distance</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/feature_ranking_abstract.h</spec_file>
<description>
This is a function that simply finds the average squared distance between all
pairs of a set of data samples. It is often convenient to use the reciprocal
of this value as the estimate of the gamma parameter of the
<a href="#radial_basis_kernel">radial_basis_kernel</a>.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>batch</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/pegasos_abstract.h</spec_file>
<description>
This is a convenience function for creating
<a href="#batch_trainer">batch_trainer</a> objects.
</description>
<examples>
<example>svm_pegasos_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>verbose_batch</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/pegasos_abstract.h</spec_file>
<description>
This is a convenience function for creating
<a href="#batch_trainer">batch_trainer</a> objects. This function
generates a batch_trainer that will print status messages to standard
output so that you can observe the progress of a training algorithm.
</description>
<examples>
<example>svm_pegasos_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>batch_cached</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/pegasos_abstract.h</spec_file>
<description>
This is a convenience function for creating
<a href="#batch_trainer">batch_trainer</a> objects that are setup
to use a kernel matrix cache.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>verbose_batch_cached</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/pegasos_abstract.h</spec_file>
<description>
This is a convenience function for creating
<a href="#batch_trainer">batch_trainer</a> objects. This function
generates a batch_trainer that will print status messages to standard
output so that you can observe the progress of a training algorithm.
It will also be configured to use a kernel matrix cache.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>batch_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/pegasos_abstract.h</spec_file>
<description>
This is a batch trainer object that is meant to wrap online trainer objects
that create <a href="#decision_function">decision_functions</a>. It
turns an online learning algorithm such as <a href="#svm_pegasos">svm_pegasos</a>
into a batch learning object. This allows you to use objects like
svm_pegasos with functions (e.g. <a href="#cross_validate_trainer">cross_validate_trainer</a>)
that expect batch mode training objects.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>null_trainer_type</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/null_trainer_abstract.h</spec_file>
<description>
This object is a simple tool for turning a <a href="#decision_function">decision_function</a>
(or any object with an interface compatible with decision_function)
into a trainer object that always returns the original decision
function when you try to train with it.
<p>
dlib contains a few "training post processing" algorithms (e.g.
<a href="#reduced">reduced</a> and <a href="#reduced2">reduced2</a>). These tools
take in a trainer object,
tell it to perform training, and then they take the output decision
function and do some kind of post processing to it. The null_trainer_type
object is useful because you can use it to run an already
learned decision function through the training post processing
algorithms by turning a decision function into a null_trainer_type
and then giving it to a post processor.
</p>
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>null_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/null_trainer_abstract.h</spec_file>
<description>
This is a convenience function for creating
<a href="#null_trainer_type">null_trainer_type</a>
objects.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>roc_c1_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/roc_trainer_abstract.h</spec_file>
<description>
This is a convenience function for creating
<a href="#roc_trainer_type">roc_trainer_type</a> objects that are
setup to pick a point on the ROC curve with respect to the +1 class.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>roc_c2_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/roc_trainer_abstract.h</spec_file>
<description>
This is a convenience function for creating
<a href="#roc_trainer_type">roc_trainer_type</a> objects that are
setup to pick a point on the ROC curve with respect to the -1 class.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>roc_trainer_type</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/roc_trainer_abstract.h</spec_file>
<description>
This object is a simple trainer post processor that allows you to
easily adjust the bias term in a trained decision_function object.
That is, this object lets you pick a point on the ROC curve and
it will adjust the bias term appropriately.
<p>
So for example, suppose you wanted to set the bias term so that
the accuracy of your decision function on +1 labeled samples was 99%.
To do this you would use an instance of this object declared as follows:
<tt>roc_trainer_type<trainer_type>(your_trainer, 0.99, +1);</tt>
</p>
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>reduced_decision_function_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/reduced_abstract.h</spec_file>
<description>
This is a batch trainer object that is meant to wrap other batch trainer objects
that create <a href="#decision_function">decision_function</a> objects.
It performs post processing on the output decision_function objects
with the intent of representing the decision_function with fewer
basis vectors.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>reduced</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/reduced_abstract.h</spec_file>
<description>
This is a convenience function for creating
<a href="#reduced_decision_function_trainer">reduced_decision_function_trainer</a>
objects.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>reduced2</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/reduced_abstract.h</spec_file>
<description>
This is a convenience function for creating
<a href="#reduced_decision_function_trainer2">reduced_decision_function_trainer2</a>
objects.
</description>
<examples>
<example>svm_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>reduced_decision_function_trainer2</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/reduced_abstract.h</spec_file>
<description>
<p>
This is a batch trainer object that is meant to wrap other batch trainer objects
that create <a href="#decision_function">decision_function</a> objects.
It performs post processing on the output decision_function objects
with the intent of representing the decision_function with fewer
basis vectors.
</p>
<p>
It begins by performing the same post processing as
the <a href="#reduced_decision_function_trainer">reduced_decision_function_trainer</a>
object but it also performs a global gradient based optimization
to further improve the results. The gradient based optimization is
implemented using the <a href="#approximate_distance_function">approximate_distance_function</a> routine.
</p>
</description>
<examples>
<example>svm_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>approximate_distance_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/reduced_abstract.h</spec_file>
<description>
This function attempts to find a <a href="#distance_function">distance_function</a> object which is close
to a target distance_function. That is, it searches for an X such that target(X) is
minimized. Critically, X may be set to use fewer basis vectors than the target.
<p>The optimization begins with an initial guess supplied by the user
and searches for an X which locally minimizes target(X). Since
this problem can have many local minima the quality of the starting point
can significantly influence the results. </p>
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>test_binary_decision_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
<description>
Tests a <a href="#decision_function">decision_function</a> that represents a binary decision function and
returns the test accuracy.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>test_multiclass_decision_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/cross_validate_multiclass_trainer_abstract.h</spec_file>
<description>
Tests a multiclass decision function (e.g. <a href="#one_vs_one_decision_function">one_vs_one_decision_function</a>)
and returns a confusion matrix describing the results.
</description>
<examples>
<example>multiclass_classification_ex.cpp.html</example>
<example>custom_trainer_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>cross_validate_trainer_threaded</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/svm_threaded_abstract.h</spec_file>
<description>
Performs k-fold cross validation on a user supplied binary classification trainer object such
as the <a href="#svm_nu_trainer">svm_nu_trainer</a> or <a href="#rbf_network_trainer">rbf_network_trainer</a>.
This function does the same thing as <a href="#cross_validate_trainer">cross_validate_trainer</a>
except this function also allows you to specify how many threads of execution to use.
So you can use this function to take advantage of a multi-core system to perform
cross validation faster.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>cross_validate_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
<description>
Performs k-fold cross validation on a user supplied binary classification trainer object such
as the <a href="#svm_nu_trainer">svm_nu_trainer</a> or <a href="#rbf_network_trainer">rbf_network_trainer</a>.
</description>
<examples>
<example>svm_ex.cpp.html</example>
<example>model_selection_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>cross_validate_multiclass_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/cross_validate_multiclass_trainer_abstract.h</spec_file>
<description>
Performs k-fold cross validation on a user supplied multiclass classification trainer object such
as the <a href="#one_vs_one_trainer">one_vs_one_trainer</a>. The result is described by a
confusion matrix.
</description>
<examples>
<example>multiclass_classification_ex.cpp.html</example>
<example>custom_trainer_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>cross_validate_regression_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/cross_validate_regression_trainer_abstract.h</spec_file>
<description>
Performs k-fold cross validation on a user supplied regression trainer object such
as the <a href="#svr_trainer">svr_trainer</a> and returns the mean squared error
and R-squared value.
</description>
<examples>
<example>svr_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>cross_validate_sequence_labeler</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/cross_validate_sequence_labeler_abstract.h</spec_file>
<description>
Performs k-fold cross validation on a user supplied sequence labeling trainer object such
as the <a href="#structural_sequence_labeling_trainer">structural_sequence_labeling_trainer</a>
and returns a confusion matrix describing the results.
</description>
<examples>
<example>sequence_labeler_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>cross_validate_sequence_segmenter</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/cross_validate_sequence_segmenter_abstract.h</spec_file>
<description>
Performs k-fold cross validation on a user supplied sequence segmentation trainer object such
as the <a href="#structural_sequence_segmentation_trainer">structural_sequence_segmentation_trainer</a>
and returns the resulting precision, recall, and F1-score.
</description>
<examples>
<example>sequence_segmenter.py.html</example>
<example>sequence_segmenter_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>cross_validate_assignment_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/cross_validate_assignment_trainer_abstract.h</spec_file>
<description>
Performs k-fold cross validation on a user supplied assignment trainer object such
as the <a href="#structural_assignment_trainer">structural_assignment_trainer</a>
and returns the fraction of assignments predicted correctly.
</description>
<examples>
<example>assignment_learning_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>cross_validate_track_association_trainer</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/cross_validate_track_association_trainer_abstract.h</spec_file>
<description>
Performs k-fold cross validation on a user supplied track association trainer object such
as the <a href="#structural_track_association_trainer">structural_track_association_trainer</a>
and returns the fraction of detections which were correctly associated to their tracks.
</description>
<examples>
<example>learning_to_track_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>cross_validate_graph_labeling_trainer</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/cross_validate_graph_labeling_trainer_abstract.h</spec_file>
<description>
Performs k-fold cross validation on a user supplied graph labeling trainer object such
as the <a href="#structural_graph_labeling_trainer">structural_graph_labeling_trainer</a>
and returns the fraction of assignments predicted correctly.
</description>
<examples>
<example>graph_labeling_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>cross_validate_ranking_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/ranking_tools_abstract.h</spec_file>
<description>
Performs k-fold cross validation on a user supplied ranking trainer object such
as the <a href="#svm_rank_trainer">svm_rank_trainer</a>
and returns the fraction of ranking pairs ordered correctly as well as the mean
average precision.
</description>
<examples>
<example>svm_rank_ex.cpp.html</example>
<example>svm_rank.py.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>test_sequence_labeler</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/cross_validate_sequence_labeler_abstract.h</spec_file>
<description>
Tests a <a href="#sequence_labeler">sequence_labeler</a> on a set of data
and returns a confusion matrix describing the results.
</description>
<examples>
<example>sequence_labeler_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>test_sequence_segmenter</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/cross_validate_sequence_segmenter_abstract.h</spec_file>
<description>
Tests a <a href="#sequence_segmenter">sequence_segmenter</a> on a set of data
and returns the resulting precision, recall, and F1-score.
</description>
<examples>
<example>sequence_segmenter.py.html</example>
<example>sequence_segmenter_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>test_assignment_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/cross_validate_assignment_trainer_abstract.h</spec_file>
<description>
Tests an <a href="#assignment_function">assignment_function</a> on a set of data
and returns the fraction of assignments predicted correctly.
</description>
<examples>
<example>assignment_learning_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>test_track_association_function</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/cross_validate_track_association_trainer_abstract.h</spec_file>
<description>
Tests a <a href="#track_association_function">track_association_function</a> on a set of data
and returns the fraction of detections which were correctly associated to their tracks.
</description>
<examples>
<example>learning_to_track_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>test_graph_labeling_function</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/cross_validate_graph_labeling_trainer_abstract.h</spec_file>
<description>
Tests a <a href="#graph_labeler">graph_labeler</a> on a set of data
and returns the fraction of labels predicted correctly.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>average_precision</name>
<file>dlib/statistics.h</file>
<spec_file link="true">dlib/statistics/average_precision_abstract.h</spec_file>
<description>
This function computes the average precision of a ranking.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>equal_error_rate</name>
<file>dlib/statistics.h</file>
<spec_file link="true">dlib/statistics/lda_abstract.h</spec_file>
<description>
This function finds a threshold that best separates the elements of two
vectors by selecting the threshold with equal error rate. It also reports
the value of the equal error rate.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>compute_roc_curve</name>
<file>dlib/statistics.h</file>
<spec_file link="true">dlib/statistics/lda_abstract.h</spec_file>
<description>
This function computes a ROC curve (receiver operating characteristic curve).
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>test_ranking_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/ranking_tools_abstract.h</spec_file>
<description>
Tests a <a href="#decision_function">decision_function</a>'s ability to correctly
rank a dataset and returns the resulting ranking accuracy and mean average precision metrics.
</description>
<examples>
<example>svm_rank_ex.cpp.html</example>
<example>svm_rank.py.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>test_shape_predictor</name>
<file>dlib/image_processing.h</file>
<spec_file link="true">dlib/image_processing/shape_predictor_abstract.h</spec_file>
<description>
Tests a <a href="imaging.html#shape_predictor">shape_predictor</a>'s ability to correctly
predict the part locations of objects. The output is the average distance (measured in pixels) between
each part and its true location. You can optionally normalize each distance using a
user supplied scale. For example, when performing face landmarking, you might want to
normalize the distances by the interocular distance.
</description>
<examples>
<example>train_shape_predictor_ex.cpp.html</example>
<example>train_shape_predictor.py.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>cross_validate_object_detection_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/cross_validate_object_detection_trainer_abstract.h</spec_file>
<description>
Performs k-fold cross validation on a user supplied object detection trainer such
as the <a href="#structural_object_detection_trainer">structural_object_detection_trainer</a>
and returns the precision and recall.
</description>
<examples>
<example>object_detector_ex.cpp.html</example>
<example>object_detector_advanced_ex.cpp.html</example>
<example>train_object_detector.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>test_object_detection_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/cross_validate_object_detection_trainer_abstract.h</spec_file>
<description>
Tests an object detector such
as the <a href="imaging.html#object_detector">object_detector</a>
and returns the precision and recall.
</description>
<examples>
<example>fhog_object_detector_ex.cpp.html</example>
<example>object_detector_ex.cpp.html</example>
<example>object_detector_advanced_ex.cpp.html</example>
<example>train_object_detector.cpp.html</example>
<example>dnn_mmod_ex.cpp.html</example>
<example>dnn_mmod_train_find_cars_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>test_regression_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/cross_validate_regression_trainer_abstract.h</spec_file>
<description>
Tests a regression function (e.g. <a href="#decision_function">decision_function</a>)
and returns the mean squared error and R-squared value.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>structural_svm_problem</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/structural_svm_problem_abstract.h</spec_file>
<description>
This object, when used with the <a href="optimization.html#oca">oca</a> optimizer, is a tool
for solving the optimization problem associated
with a structural support vector machine. A structural SVM is a supervised
machine learning method for learning to predict complex outputs. This is
contrasted with a binary classifier which makes only simple yes/no
predictions. A structural SVM, on the other hand, can learn to predict
complex outputs such as entire parse trees or DNA sequence alignments. To
do this, it learns a function F(x,y) which measures how well a particular
data sample x matches a label y. When used for prediction, the best label
for a new x is given by the y which maximizes F(x,y).
<br/>
<br/>
For an introduction to structured support vector machines you should consult
the following paper:
<blockquote>
Predicting Structured Objects with Support Vector Machines by
Thorsten Joachims, Thomas Hofmann, Yisong Yue, and Chun-nam Yu
</blockquote>
For a more detailed discussion of the particular algorithm implemented by this
object see the following paper:
<blockquote>
T. Joachims, T. Finley, Chun-Nam Yu, Cutting-Plane Training of Structural SVMs,
Machine Learning, 77(1):27-59, 2009.
</blockquote>
Note that this object is essentially a tool for solving the 1-Slack structural
SVM with margin-rescaling. Specifically, see Algorithm 3 in the above referenced
paper.
<br/><br/>
Finally, for a very detailed introduction to this subject, you should consider the book:
<blockquote>
<i><a href="http://www.nowozin.net/sebastian/papers/nowozin2011structured-tutorial.pdf">Structured
Prediction and Learning in Computer Vision</a></i> by Sebastian Nowozin and
Christoph H. Lampert
</blockquote>
</description>
<examples>
<example>svm_struct.py.html</example>
<example>svm_struct_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>structural_svm_problem_threaded</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/structural_svm_problem_threaded_abstract.h</spec_file>
<description>
This is just a version of the <a href="#structural_svm_problem">structural_svm_problem</a>
which is capable of using multiple cores/threads at a time. You should use it if
you have a multi-core CPU and the separation oracle takes a long time to compute. Or even better, if you
have multiple computers then you can use the <a href="#svm_struct_controller_node">svm_struct_controller_node</a>
to distribute the work across many computers.
</description>
<examples>
<example>svm_struct_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>structural_svm_object_detection_problem</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/structural_svm_object_detection_problem_abstract.h</spec_file>
<description>
This object is a tool for learning the parameter vector needed to use
a <a href="imaging.html#scan_fhog_pyramid">scan_fhog_pyramid</a>,
<a href="imaging.html#scan_image_pyramid">scan_image_pyramid</a>,
<a href="imaging.html#scan_image_boxes">scan_image_boxes</a>, or
<a href="imaging.html#scan_image_custom">scan_image_custom</a> object.
<p>
It learns the parameter vector by formulating the problem as a <a
href="#structural_svm_problem">structural SVM problem</a>.
The exact details of the method are described in the paper
<a href="http://arxiv.org/abs/1502.00046">Max-Margin Object Detection</a> by Davis E. King.
</p>
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>structural_svm_sequence_labeling_problem</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/structural_svm_sequence_labeling_problem_abstract.h</spec_file>
<description>
This object is a tool for learning the weight vector needed to use
a <a href="#sequence_labeler">sequence_labeler</a> object.
It learns the parameter vector by formulating the problem as a
<a href="#structural_svm_problem">structural SVM problem</a>.
The general approach is discussed in the paper:
<blockquote>
Hidden Markov Support Vector Machines by
Y. Altun, I. Tsochantaridis, T. Hofmann
</blockquote>
While the particular optimization strategy used is the method from:
<blockquote>
T. Joachims, T. Finley, Chun-Nam Yu, Cutting-Plane Training of
Structural SVMs, Machine Learning, 77(1):27-59, 2009.
</blockquote>
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>structural_svm_assignment_problem</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/structural_svm_assignment_problem_abstract.h</spec_file>
<description>
This object is a tool for learning the parameters needed to use
an <a href="#assignment_function">assignment_function</a> object.
It learns the parameters by formulating the problem as a
<a href="#structural_svm_problem">structural SVM problem</a>.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>structural_svm_graph_labeling_problem</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/structural_svm_graph_labeling_problem_abstract.h</spec_file>
<description>
This object is a tool for learning the weight vectors needed to use
a <a href="#graph_labeler">graph_labeler</a> object.
It learns the parameter vectors by
formulating the problem as a <a href="#structural_svm_problem">structural SVM problem</a>.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>structural_object_detection_trainer</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/structural_object_detection_trainer_abstract.h</spec_file>
<description>
This object is a tool for learning to detect objects in images based on a set of labeled images.
The training procedure produces an <a href="imaging.html#object_detector">object_detector</a> which
can be used to predict the locations of objects in new images.
It learns the parameter vector by formulating the problem as a <a
href="#structural_svm_problem">structural SVM problem</a>.
The exact details of the method are described in the paper
<a href="http://arxiv.org/abs/1502.00046">Max-Margin Object Detection</a> by Davis E. King.
<p>
Note that this is just a convenience wrapper around the
<a href="#structural_svm_object_detection_problem">structural_svm_object_detection_problem</a>
to make it look similar to all the other trainers in dlib.
</p>
</description>
<examples>
<example>fhog_object_detector_ex.cpp.html</example>
<example>object_detector_ex.cpp.html</example>
<example>object_detector_advanced_ex.cpp.html</example>
<example>train_object_detector.cpp.html</example>
<example>train_object_detector.py.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>structural_sequence_labeling_trainer</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/structural_sequence_labeling_trainer_abstract.h</spec_file>
<description>
This object is a tool for learning to do sequence labeling based
on a set of training data. The training procedure produces a
<a href="#sequence_labeler">sequence_labeler</a> object which can
be use to predict the labels of new data sequences.
<p>
Note that this is just a convenience wrapper around the
<a href="#structural_svm_sequence_labeling_problem">structural_svm_sequence_labeling_problem</a>
to make it look similar to all the other trainers in dlib.
</p>
</description>
<examples>
<example>sequence_labeler_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>structural_sequence_segmentation_trainer</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/structural_sequence_segmentation_trainer_abstract.h</spec_file>
<description>
This object is a tool for learning to do sequence segmentation based on a
set of training data. The training procedure produces a <a href="#sequence_segmenter">sequence_segmenter</a>
object which can be used to identify the sub-segments of new data sequences.
<p>
This object internally uses the <a href="#structural_sequence_labeling_trainer">structural_sequence_labeling_trainer</a>
to solve the learning problem.
</p>
</description>
<examples>
<example>sequence_segmenter.py.html</example>
<example>sequence_segmenter_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>structural_graph_labeling_trainer</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/structural_graph_labeling_trainer_abstract.h</spec_file>
<description>
This object is a tool for learning to solve a graph labeling problem based
on a training dataset of example labeled <a href="containers.html#graph">graphs</a>.
The training procedure produces a <a href="#graph_labeler">graph_labeler</a> object
which can be used to predict the labelings of new graphs.
<p>
To elaborate, a graph labeling problem is a task to learn a binary classifier which
predicts the label of each node in a graph. Additionally, we have information in
the form of edges between nodes where edges are present when we believe the
linked nodes are likely to have the same label. Therefore, part of a graph labeling
problem is to learn to score each edge in terms of how strongly the edge should enforce
labeling consistency between its two nodes.
</p>
<p>
Note that this is just a convenience wrapper around the
<a href="#structural_svm_graph_labeling_problem">structural_svm_graph_labeling_problem</a>
to make it look similar to all the other trainers in dlib. You might also
consider reading the book
<i><a href="http://www.nowozin.net/sebastian/papers/nowozin2011structured-tutorial.pdf">Structured
Prediction and Learning in Computer Vision</a></i> by Sebastian
Nowozin and Christoph H. Lampert since it contains a good introduction to machine learning
methods such as the algorithm implemented by the structural_graph_labeling_trainer.
</p>
</description>
<examples>
<example>graph_labeling_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>structural_assignment_trainer</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/structural_assignment_trainer_abstract.h</spec_file>
<description>
This object is a tool for learning to solve an assignment problem based
on a training dataset of example assignments. The training procedure produces an
<a href="#assignment_function">assignment_function</a> object which can be used
to predict the assignments of new data.
Note that this is just a convenience wrapper around the
<a href="#structural_svm_assignment_problem">structural_svm_assignment_problem</a>
to make it look similar to all the other trainers in dlib.
</description>
<examples>
<example>assignment_learning_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>structural_track_association_trainer</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/structural_track_association_trainer_abstract.h</spec_file>
<description>
This object is a tool for learning to solve a track association problem. That
is, it takes in a set of training data and outputs a
<a href="#track_association_function">track_association_function</a>
you can use to do detection to track association.
</description>
<examples>
<example>learning_to_track_ex.cpp.html</example>
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>svm_struct_controller_node</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/structural_svm_distributed_abstract.h</spec_file>
<description>
This object is a tool for distributing the work involved in solving a
<a href="#structural_svm_problem">structural_svm_problem</a> across many computers.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>svm_struct_processing_node</name>
<file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/structural_svm_distributed_abstract.h</spec_file>
<description>
This object is a tool for distributing the work involved in solving a
<a href="#structural_svm_problem">structural_svm_problem</a> across many computers.
</description>
</component>
<!-- ************************************************************************* -->
</components>
<!-- ************************************************************************* -->
</doc>
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