Statistical mechanics of support vector networks

Rainer Dietrich, Manfred Opper, Haim Sompolinsky

Research output: Contribution to journalArticlepeer-review

113 Scopus citations

Abstract

Using methods of statistical physics, we investigate the generalization performance of support vector machines (SVMs), which have been recently introduced as a general alternative to neural networks. For nonlinear classification rules, the generalization error saturates on a plateau when the number of examples is too small to properly estimate the coefficients of the nonlinear part. When trained on simple rules, we find that SVMs overfit only weakly. The performance of SVMs is strongly enhanced when the distribution of the inputs has a gap in feature space.

Original languageEnglish
Pages (from-to)2975-2978
Number of pages4
JournalPhysical Review Letters
Volume82
Issue number14
DOIs
StatePublished - 1999

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