Which Is the Best Multiclass SVM Method? An Empirical Study

  • Kai-Bo Duan
  • S. Sathiya Keerthi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3541)

Abstract

Multiclass SVMs are usually implemented by combining several two-class SVMs. The one-versus-all method using winner-takes-all strategy and the one-versus-one method implemented by max-wins voting are popularly used for this purpose. In this paper we give empirical evidence to show that these methods are inferior to another one-versus-one method: one that uses Platt’s posterior probabilities together with the pairwise coupling idea of Hastie and Tibshirani. The evidence is particularly strong when the training dataset is sparse.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Kai-Bo Duan
    • 1
  • S. Sathiya Keerthi
    • 2
  1. 1.BioInformatics Research CentreNanyang Technological UniversitySingapore
  2. 2.Yahoo! Research LabsPasadenaUSA

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