In this example a support vector regression algorithm is trained on a
real-valued toy data set. The underlying library used for the SVR training is
SVM^light. The SVR is trained with regularization parameter C=1 and a gaussian
kernel with width=2.1. The the label of both the train and the test data are
fetched via svr.classify().get_labels().

For more details on the SVM^light see
 T. Joachims. Making large-scale SVM learning practical. In Advances in Kernel
 Methods -- Support Vector Learning, pages 169-184. MIT Press, Cambridge, MA USA, 1999.
