In this example a two-class linear support vector machine classifier 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=1.2 and the bias term in the classification rule
switched off. The solver iterates until it finds the epsilon-precise solution
(epsilon=1e-5) or the maximal training time (max_train_time=60 seconds) is exceeded.

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
