Research Article

A tutorial on ν-support vector machines

  1. Pai-Hsuen Chen1,
  2. Chih-Jen Lin1,
  3. Bernhard Schölkopf2,†,*

Article first published online: 23 MAR 2005

DOI: 10.1002/asmb.537

Issue

Applied Stochastic Models in Business and Industry

Applied Stochastic Models in Business and Industry

Special Issue: Statistical Learning

Volume 21, Issue 2, pages 111–136, March/April 2005

Additional Information

How to Cite

Chen, P.-H., Lin, C.-J. and Schölkopf, B. (2005), A tutorial on ν-support vector machines. Appl. Stochastic Models Bus. Ind., 21: 111–136. doi: 10.1002/asmb.537

Author Information

  1. 1

    Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan

  2. 2

    Max Planck Institute for Biological Cybernetics, Tübingen, Germany

  1. †

    Parts of the present article are based on [1].

*Max Planck Institute for Biological Cybernetics, Tübingen, Germany

Publication History

  1. Issue published online: 23 MAR 2005
  2. Article first published online: 23 MAR 2005

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Keywords:

  • ν-support vector machines;
  • support vector regression;
  • support vector implementation;
  • statistical learning theory;
  • positive definite kernels

Abstract

We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so-called ν-SVM, including details of the algorithm and its implementation, theoretical results, and practical applications. Copyright © 2005 John Wiley & Sons, Ltd.

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