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
LIBSVM. The SVR is trained with regularization parameter C=1 and a gaussian
kernel with width=2.1. The labels of both the train and the test data are
fetched via svr.classify().get_labels().

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