Constructing boosting algorithms from SVMs: An application to one-class classification

Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Klaus Muller

Research output: Contribution to journalArticle

166 Citations (Scopus)

Abstract

We show via an equivalence of mathematical programs that a support vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation procedure for a new algorithm-one-class leveraging-starting from the one-class support vector machine (1-SVM). This is a first step toward unsupervised learning in a boosting framework. Building on so-called barrier methods known from the theory of constrained optimization, it returns a function, written as a convex combination of base hypotheses, that characterizes whether a given test point is likely to have been generated from the distribution underlying the training data. Simulations on one-class classification problems demonstrate the usefulness of our approach.

Original languageEnglish
Pages (from-to)1184-1199
Number of pages16
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume24
Issue number9
DOIs
Publication statusPublished - 2002 Sep 1
Externally publishedYes

Keywords

  • Boosting
  • Novelty detection
  • One-class classification
  • SVMs
  • Unsupervised learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Constructing boosting algorithms from SVMs: An application to one-class classification'. Together they form a unique fingerprint.

  • Cite this