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Support vector machine software

Part 4. Bioinformatics

4.8. Modern Programming Paradigms in Biology

Basic Techniques and Approaches

  1. William Stafford Noble

Published Online: 15 JAN 2005

DOI: 10.1002/047001153X.g409416

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

How to Cite

Noble, W. S. 2005. Support vector machine software. Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. 4:4.8:110.

Author Information

  1. University of Washington, Seattle, WA, USA

Publication History

  1. Published Online: 15 JAN 2005

Abstract

The support vector machine has been used successfully to perform pattern recognition on many different types of biological data. However, applying the algorithm to a new problem domain is often nontrivial, because the algorithm offers many tunable parameters. Most important among these are the choice of the kernel function and the strength of the soft margin. This chapter describes the SVM algorithm and gives some practical advice for applying the algorithm to real biological data.

Keywords:

  • support vector machine;
  • pattern recognition;
  • classification;
  • microarray analysis;
  • machine learning