Tutorial: Support Vector Machines and Kernels for Computational Biology

Asa Ben-Hur, Cheng Soon Ong, Sören Sonnenburg, Bernhard Schölkopf, and Gunnar Rätsch

  • Basics
  • Tutorial (PDF)
  • Software
  • Galaxy Webservice
  • Splice Site Prediction
  • Examples
  • Imprint

All computational results in the tutorial were generated using the Shogun-based Easysvm tool written in python. This toolbox is also available at a Galaxy-based webservice. An alternative implementation using PyML is also available.

Below we provide a minimal list of SVM packages. A fuller list can be found on the Kernel machines website and the Machine Learning Open Source Software site.

  • Easysvm an easy-to-use SVM toolbox based on python and the Shogun toolbox
  • PyML an easy-to-use python-based SVM toolbox
  • Shogun toolbox a powerful toolbox for large scale data analysis including many SVM implementations with support for python, R, matlab and octave.
  • LibSVM an SVM library with a graphic interface
  • SVM-Light an implementation of SVMs in C

Copyright © 2008 A. Ben-Hur, C.S. Ong, S. Sonnenburg, B. Schölkopf, and G. Rätsch