Machine Learning

, Volume 20, Issue 3, pp 273–297

Support-Vector Networks

  • Corinna Cortes
  • Vladimir Vapnik
Article

DOI: 10.1023/A:1022627411411

Cite this article as:
Cortes, C. & Vapnik, V. Machine Learning (1995) 20: 273. doi:10.1023/A:1022627411411

Abstract

The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.

High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

pattern recognitionefficient learning algorithmsneural networksradial basis function classifierspolynomial classifiers
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Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Corinna Cortes
    • 1
  • Vladimir Vapnik
  1. 1.AT&T Bell LabsHolmdelUSA