diff --git a/examples/model_selection_ex.cpp b/examples/model_selection_ex.cpp
index 5bd360c7c..81a975c18 100644
--- a/examples/model_selection_ex.cpp
+++ b/examples/model_selection_ex.cpp
@@ -78,36 +78,37 @@ int main() try
 
 
     // Now that we have some data we want to train on it.  We are going to train a
-    // binary SVM with the RBF kernel to classify the data.  However, there are two
-    // parameters to the training.  These are the nu and gamma parameters.  Our choice
-    // for these parameters will influence how good the resulting decision function is.
-    // To test how good a particular choice of these parameters is we can use the
+    // binary SVM with the RBF kernel to classify the data.  However, there are
+    // three parameters to the training.  These are the SVM C parameters for each
+    // class and the RBF kernel's gamma parameter.  Our choice for these
+    // parameters will influence how good the resulting decision function is.  To
+    // test how good a particular choice of these parameters is we can use the
     // cross_validate_trainer() function to perform n-fold cross validation on our
-    // training data.  However, there is a problem with the way we have sampled our
-    // distribution above.  The problem is that there is a definite ordering to the
-    // samples.  That is, the first half of the samples look like they are from a
-    // different distribution than the second half.  This would screw up the cross
-    // validation process, but we can fix it by randomizing the order of the samples
-    // with the following function call.
+    // training data.  However, there is a problem with the way we have sampled
+    // our distribution above.  The problem is that there is a definite ordering
+    // to the samples.  That is, the first half of the samples look like they are
+    // from a different distribution than the second half.  This would screw up
+    // the cross validation process, but we can fix it by randomizing the order of
+    // the samples with the following function call.
     randomize_samples(samples, labels);
 
 
-
     // And now we get to the important bit.  Here we define a function,
     // cross_validation_score(), that will do the cross-validation we
     // mentioned and return a number indicating how good a particular setting
-    // of gamma and nu is.
-    auto cross_validation_score = [&](const double gamma, const double nu) 
+    // of gamma, c1, and c2 is.
+    auto cross_validation_score = [&](const double gamma, const double c1, const double c2) 
     {
         // Make a RBF SVM trainer and tell it what the parameters are supposed to be.
         typedef radial_basis_kernel<sample_type> kernel_type;
-        svm_nu_trainer<kernel_type> trainer;
+        svm_c_trainer<kernel_type> trainer;
         trainer.set_kernel(kernel_type(gamma));
-        trainer.set_nu(nu);
+        trainer.set_c_class1(c1);
+        trainer.set_c_class2(c2);
 
         // Finally, perform 10-fold cross validation and then print and return the results.
         matrix<double> result = cross_validate_trainer(trainer, samples, labels, 10);
-        cout << "gamma: " << setw(11) << gamma << "  nu: " << setw(11) << nu <<  "  cross validation accuracy: " << result;
+        cout << "gamma: " << setw(11) << gamma << "  c1: " << setw(11) << c1 <<  "  c2: " << setw(11) << c2 <<  "  cross validation accuracy: " << result;
 
         // Now return a number indicating how good the parameters are.  Bigger is
         // better in this example.  Here I'm returning the harmonic mean between the
@@ -119,33 +120,26 @@ int main() try
         return 2*prod(result)/sum(result);
     };
 
-    // The nu parameter has a maximum value that is dependent on the ratio of the +1 to -1
-    // labels in the training data.  This function finds that value.  The 0.999 is here
-    // because the maximum allowable nu is strictly less than the value returned by
-    // maximum_nu().  So shrinking the limit a little will prevent us from hitting it.
-    const double max_nu = 0.999*maximum_nu(labels);
-
 
     // And finally, we call this global optimizer that will search for the best parameters.
-    // It will call cross_validation_score() 50 times with different settings and return
+    // It will call cross_validation_score() 30 times with different settings and return
     // the best parameter setting it finds.  find_max_global() uses a global optimization
     // method based on a combination of non-parametric global function modeling and
     // quadratic trust region modeling to efficiently find a global maximizer.  It usually
     // does a good job with a relatively small number of calls to cross_validation_score().
     // In this example, you should observe that it finds settings that give perfect binary
-    // classification on the data.
+    // classification of the data.
     auto result = find_max_global(cross_validation_score, 
-                                  {1e-5, 1e-5},  // lower bound constraints on gamma and nu, respectively
-                                  {100, max_nu}, // upper bound constraints on gamma and nu, respectively
-                                  max_function_calls(50));
+                                  {1e-5, 1e-5, 1e-5},  // lower bound constraints on gamma, c1, and c2, respectively
+                                  {100,  1e6,  1e6},   // upper bound constraints on gamma, c1, and c2, respectively
+                                  max_function_calls(30));
 
     double best_gamma = result.x(0);
-    double best_nu    = result.x(1);
+    double best_c1    = result.x(1);
+    double best_c2    = result.x(2);
 
     cout << " best cross-validation score: " << result.y << endl;
-    cout << " best gamma: " << best_gamma << "   best nu: " << best_nu << endl;
-
-
+    cout << " best gamma: " << best_gamma << "   best c1: " << best_c1 << "    best c2: "<< best_c2  << endl;
 }
 catch (exception& e)
 {