In this example a multi-class support vector machine is trained on a toy data
set and the trained classifier is used to predict labels of test examples.
The training algorithm is based on BSVM formulation (L2-soft margin
and the bias added to the objective function) which is solved by the Improved
Mitchell-Demyanov-Malozemov algorithm. The training algorithm uses the Gaussian
kernel of width 2.1 and the regularization constant C=1.2. The bias term of the
classification rule is not used. The solver stops if the relative duality gap
falls below 1e-5 and it uses 10MB for kernel cache.

For more details on the used SVM solver see
 V.Franc: Optimization Algorithms for Kernel Methods. Research report.
 CTU-CMP-2005-22. CTU FEL Prague. 2005.
 ftp://cmp.felk.cvut.cz/pub/cmp/articles/franc/Franc-PhD.pdf .

