An introduction to kernel-based learning algorithms

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

Research output: Contribution to journalArticle

2587 Citations (Scopus)

Abstract

This paper provides an introduction to support vector machines (SVMs), kernel Fisher discriminant analysis, and kernel principal component analysis (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 optical character recognition (OCR) and DNA analysis.

Original languageEnglish
Pages (from-to)181-201
Number of pages21
JournalIEEE Transactions on Neural Networks
Volume12
Issue number2
DOIs
Publication statusPublished - 2001 Mar 1
Externally publishedYes

Fingerprint

Optical character recognition
Discriminant analysis
Principal component analysis
Learning algorithms
Support vector machines
Learning Algorithm
DNA
Learning
kernel
Discriminant Analysis
Principal Component Analysis
Fisher Discriminant Analysis
Kernel Principal Component Analysis
Character Recognition
Feature Space
Support Vector Machine
Scenarios
Recognition (Psychology)

Keywords

  • Boosting
  • Fisher's discriminant
  • Kernel methods
  • Kernel PCA
  • Mathematical programming machines
  • Mercer kernels
  • Principal component analysis (PCA)
  • Single-class classification
  • Support vector machines (SVMs)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Hardware and Architecture

Cite this

Muller, K., Mika, S., Rätsch, G., Tsuda, K., & Schölkopf, B. (2001). An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 12(2), 181-201. https://doi.org/10.1109/72.914517

An introduction to kernel-based learning algorithms. / Muller, Klaus; Mika, Sebastian; Rätsch, Gunnar; Tsuda, Koji; Schölkopf, Bernhard.

In: IEEE Transactions on Neural Networks, Vol. 12, No. 2, 01.03.2001, p. 181-201.

Research output: Contribution to journalArticle

Muller, K, Mika, S, Rätsch, G, Tsuda, K & Schölkopf, B 2001, 'An introduction to kernel-based learning algorithms', IEEE Transactions on Neural Networks, vol. 12, no. 2, pp. 181-201. https://doi.org/10.1109/72.914517
Muller K, Mika S, Rätsch G, Tsuda K, Schölkopf B. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks. 2001 Mar 1;12(2):181-201. https://doi.org/10.1109/72.914517
Muller, Klaus ; Mika, Sebastian ; Rätsch, Gunnar ; Tsuda, Koji ; Schölkopf, Bernhard. / An introduction to kernel-based learning algorithms. In: IEEE Transactions on Neural Networks. 2001 ; Vol. 12, No. 2. pp. 181-201.
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