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 Stochastic Gradient Descent (SGD) solver is
used with the SVM regularization parameter C=0.9. The number of iterations, i.e.
passes though all training examples, is set to num_iter=5 .

For more details on the SGD solver see
 L. Bottou, O. Bousquet. The tradeoff of large scale learning. In NIPS 20. MIT
 Press. 2008.
