In this example a one-class support vector machine classifier is trained on a
toy data set. The training algorithm finds a hyperplane in the RKHS which
separates the training data from the origin. The one-class classifier is
typically used to estimate the support of a high-dimesnional distribution. 
For more details see e.g. 
  B. Schoelkopf et al. Estimating the support of a high-dimensional
  distribution. Neural Computation, 13, 2001, 1443-1471. 

In the example, the one-class SVM is trained by the LIBSVM solver with the
regularization parameter C=1.2 and the Gaussian kernel of width 2.1 and the
precision parameter epsilon=1e-5 and 10MB of the kernel cache.

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


