A Bahadur representation of the linear support vector machine

Ja Yong Koo, Yoonkyung Lee, Yuwon Kim, Changyi Park

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

The support vector machine has been successful in a variety of applications. Also on the theoretical front, statistical properties of the support vector machine have been studied quite extensively with a particular attention to its Bayes risk consistency under some conditions. In this paper, we study somewhat basic statistical properties of the support vector machine yet to be investigated, namely the asymptotic behavior of the coefficients of the linear support vector machine. A Bahadur type representation of the coefficients is established under appropriate conditions, and their asymptotic normality and statistical variability are derived on the basis of the representation. These asymptotic results do not only help further our understanding of the support vector machine, but also they can be useful for related statistical inferences.

Original languageEnglish
Pages (from-to)1343-1368
Number of pages26
JournalJournal of Machine Learning Research
Volume9
StatePublished - Jul 2008

Keywords

  • Asymptotic normality
  • Bahadur representation
  • Classification
  • Convexity lemma
  • Radon transform

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