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diff --git a/ml/dlib/dlib/svm/svm_threaded_abstract.h b/ml/dlib/dlib/svm/svm_threaded_abstract.h
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+// Copyright (C) 2008  Davis E. King (davis@dlib.net)
+// License: Boost Software License   See LICENSE.txt for the full license.
+#undef DLIB_SVm_THREADED_ABSTRACT_
+#ifdef DLIB_SVm_THREADED_ABSTRACT_
+
+#include "../matrix/matrix_abstract.h"
+#include "../algs.h"
+#include "../svm.h"
+
+namespace dlib
+{
+
+// ----------------------------------------------------------------------------------------
+
+    template <
+        typename trainer_type,
+        typename in_sample_vector_type,
+        typename in_scalar_vector_type
+        >
+    const matrix<double, 1, 2, typename trainer_type::mem_manager_type> 
+    cross_validate_trainer_threaded (
+        const trainer_type& trainer,
+        const in_sample_vector_type& x,
+        const in_scalar_vector_type& y,
+        const long folds,
+        const long num_threads
+    );
+    /*!
+        requires
+            - is_binary_classification_problem(x,y) == true
+            - 1 < folds <= std::min(sum(y>0),sum(y<0))
+              (e.g. There must be at least as many examples of each class as there are folds)
+            - trainer_type == some kind of trainer object (e.g. svm_nu_trainer)
+            - num_threads > 0
+            - It must be safe for multiple trainer objects to access the elements of x from
+              multiple threads at the same time.  Note that all trainers and kernels in
+              dlib are thread safe in this regard since they do not mutate the elements of x.
+        ensures
+            - performs k-fold cross validation by using the given trainer to solve the
+              given binary classification problem for the given number of folds.
+              Each fold is tested using the output of the trainer and the average 
+              classification accuracy from all folds is returned.  
+            - uses num_threads threads of execution in doing the cross validation.  
+            - The accuracy is returned in a row vector, let us call it R.  Both 
+              quantities in R are numbers between 0 and 1 which represent the fraction 
+              of examples correctly classified.  R(0) is the fraction of +1 examples 
+              correctly classified and R(1) is the fraction of -1 examples correctly 
+              classified.
+            - The number of folds used is given by the folds argument.
+        throws
+            - any exceptions thrown by trainer.train()
+            - std::bad_alloc
+    !*/
+
+// ----------------------------------------------------------------------------------------
+
+}
+
+#endif // DLIB_SVm_THREADED_ABSTRACT_
+
+
+