In this example a two-class support vector machine classifier is trained on a
DNA splice-site detection data set and the trained classifier is used to predict
labels on test set. As training algorithm SVM^light is used with SVM
regularization parameter C=1 and the Weighted Degree kernel of the degree 20 and
a precision parameter epsilon=1e-5. The LINADD trick is used to speed up
training.

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.

For more details on the Weighted Degree kernel and the LINADD trick see
  Sonnenburg, s. and Rätsch, G. and Rieck, K. Large Scale Learning with String
  Kernels. In Bottou, Leon and Chapelle, Olivier and DeCoste, Dennis and Weston,
  Jason, editor, In Large Scale Kernel Machines, pages 73-103, MIT Press,
  Cambridge, MA. 2007.  

