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 LIBLINEAR solver is used with the SVM
regularization parameter C=1 and the bias term in the classification rule
switched off. The solver iterates until it reaches epsilon-precise
(epsilon=1.e-5) solution or the maximal training time (max_train_time=60
seconds) is exceeded.

For more details on LIBLINEAR see
    http://www.csie.ntu.edu.tw/~cjlin/liblinear/
