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diff --git a/ml/dlib/examples/rvm_regression_ex.cpp b/ml/dlib/examples/rvm_regression_ex.cpp
deleted file mode 100644
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--- a/ml/dlib/examples/rvm_regression_ex.cpp
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-// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
-/*
-    This is an example illustrating the use of the RVM regression object 
-    from the dlib C++ Library.
-
-    This example will train on data from the sinc function.
-
-*/
-
-#include <iostream>
-#include <vector>
-
-#include <dlib/svm.h>
-
-using namespace std;
-using namespace dlib;
-
-// Here is the sinc function we will be trying to learn with rvm regression 
-double sinc(double x)
-{
-    if (x == 0)
-        return 1;
-    return sin(x)/x;
-}
-
-int main()
-{
-    // Here we declare that our samples will be 1 dimensional column vectors.  
-    typedef matrix<double,1,1> sample_type;
-
-    // Now sample some points from the sinc() function
-    sample_type m;
-    std::vector<sample_type> samples;
-    std::vector<double> labels;
-    for (double x = -10; x <= 4; x += 1)
-    {
-        m(0) = x;
-        samples.push_back(m);
-        labels.push_back(sinc(x));
-    }
-
-    // Now we are making a typedef for the kind of kernel we want to use.  I picked the
-    // radial basis kernel because it only has one parameter and generally gives good
-    // results without much fiddling.
-    typedef radial_basis_kernel<sample_type> kernel_type;
-
-    // Here we declare an instance of the rvm_regression_trainer object.  This is the
-    // object that we will later use to do the training.
-    rvm_regression_trainer<kernel_type> trainer;
-
-    // Here we set the kernel we want to use for training.   The radial_basis_kernel 
-    // has a parameter called gamma that we need to determine.  As a rule of thumb, a good 
-    // gamma to try is 1.0/(mean squared distance between your sample points).  So 
-    // below we are using a similar value.   Note also that using an inappropriately large
-    // gamma will cause the RVM training algorithm to run extremely slowly.  What
-    // "large" means is relative to how spread out your data is.  So it is important
-    // to use a rule like this as a starting point for determining the gamma value
-    // if you want to use the RVM.  It is also probably a good idea to normalize your
-    // samples as shown in the rvm_ex.cpp example program.
-    const double gamma = 2.0/compute_mean_squared_distance(samples);
-    cout << "using gamma of " << gamma << endl;
-    trainer.set_kernel(kernel_type(gamma));
-
-    // One thing you can do to reduce the RVM training time is to make its
-    // stopping epsilon bigger.  However, this might make the outputs less
-    // reliable.  But sometimes it works out well.  0.001 is the default.
-    trainer.set_epsilon(0.001);
-
-    // now train a function based on our sample points
-    decision_function<kernel_type> test = trainer.train(samples, labels);
-
-    // now we output the value of the sinc function for a few test points as well as the 
-    // value predicted by our regression.
-    m(0) = 2.5; cout << sinc(m(0)) << "   " << test(m) << endl;
-    m(0) = 0.1; cout << sinc(m(0)) << "   " << test(m) << endl;
-    m(0) = -4;  cout << sinc(m(0)) << "   " << test(m) << endl;
-    m(0) = 5.0; cout << sinc(m(0)) << "   " << test(m) << endl;
-
-    // The output is as follows:
-    //using gamma of 0.05
-    //0.239389   0.240989
-    //0.998334   0.999538
-    //-0.189201   -0.188453
-    //-0.191785   -0.226516
-
-
-    // The first column is the true value of the sinc function and the second
-    // column is the output from the rvm estimate.  
-
-
-
-    // Another thing that is worth knowing is that just about everything in dlib is serializable.
-    // So for example, you can save the test object to disk and recall it later like so:
-    serialize("saved_function.dat") << test;
-
-    // Now let's open that file back up and load the function object it contains.
-    deserialize("saved_function.dat") >> test;
-
-}
-
-