An introduction to kernel-based learning algorithms

Klaus Muller, Sebastian Mika, Gunnar Rätsch, Koji Tsuda, Bernhard Schölkopf

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

Abstract

This chapter provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel PCA as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis (VC) theory and kernel feature spaces and then proceed to kernel-based learning in supervised and unsupervised scenarios, including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by finally discussing applications such as OCR and DNA analysis.

Original languageEnglish
Title of host publicationHandbook of Neural Network Signal Processing
PublisherCRC Press
Pages4-1-4-40
ISBN (Electronic)9781420038613
ISBN (Print)9780849323591
Publication statusPublished - 2001 Jan 1
Externally publishedYes

ASJC Scopus subject areas

  • Engineering(all)
  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)

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