An introduction to kernel-based learning algorithms

Klaus Robert Müller; Sebastian Mika; Gunnar Rätsch; Koji Tsuda; Bernhard Schölkopf

An introduction to kernel-based learning algorithms

Klaus Robert Müller, Sebastian Mika, Gunnar Rätsch, Koji Tsuda, Bernhard Schölkopf

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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 language	English
Title of host publication	Handbook of Neural Network Signal Processing
Publisher	CRC Press
Pages	4-1-4-40
ISBN (Electronic)	9781420038613
ISBN (Print)	9780849323591
Publication status	Published - 2001 Jan 1

ASJC Scopus subject areas

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

Cite this

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N2 - 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.

AB - 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.

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An introduction to kernel-based learning algorithms

Abstract

ASJC Scopus subject areas

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