In this example a two-class support vector machine classifier is trained on a
toy data set and the trained classifier is then used to predict labels of test
examples. As training algorithm Gradient Projection Decomposition Technique
(GPDT) is used with SVM regularization parameter C=1 and a Gaussian
kernel of width 2.1. The solver returns an epsilon-precise (epsilon=1e-5) solution.

For more details on GPDT solver see http://dm.unife.it/gpdt .

