In this example a multi-class support vector machine is trained on a toy data
set and the trained classifier is then 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. The solver stops if the
relative duality gap falls below 1e-5. 

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 .

