In this example a two-class linear support vector machine classifier (SVM) is
trained on a toy data set and the trained classifier is used to predict labels
of test examples. As training algorithm the SVMLIN solver is used with the SVM
regularization parameter C=0.9 and the bias in the classification rule switched
on and the precision parameter epsilon=1e-5. The example also shows how to
retrieve parameters (vector w and bias b)) of the trained linear classifier.

For more details on the SVMLIN solver see
 V. Sindhwani, S.S. Keerthi. Newton Methods for Fast Solution of Semi-supervised
 Linear SVMs. Large Scale Kernel Machines MIT Press (Book Chapter), 2007
