In this example a multi-class 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 LIBSVM is used with SVM regularization
parameter C=1.2 and the bias in the classification rule switched off and 
a Gaussian kernel of width 2.1 and 10MB of kernel cache and the precision 
parameter epsilon=1e-5.

For more details on LIBSVM solver see http://www.csie.ntu.edu.tw/~cjlin/libsvm/ 
