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 steepest descent subgradient algorithm
is used with the SVM regularization parameter C=1.2 and the bias in the classification
rule switched off. The solver iterates until it finds epsilon-precise solution 
(epsilon=1e-5) or the maximal training time (max_train_time=60 seconds) is exceeded. 

Note that this solver often has a tendency not to converge because the steepest descent
subgradient algorithm is oversensitive to rounding errors. Note also that this
is an unpublished work which was predecessor of the OCAS solver (see
classifier_svmocas).
