An introduction to kernel-based learning algorithms

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

doi:10.1109/72.914517

An introduction to kernel-based learning algorithms

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

Research output: Contribution to journal › Review article › peer-review

2765 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 language	English
Pages (from-to)	181-201
Number of pages	21
Journal	IEEE Transactions on Neural Networks
Volume	12
Issue number	2
DOIs	https://doi.org/10.1109/72.914517
Publication status	Published - 2001 Mar

Keywords

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

ASJC Scopus subject areas

Software
Computer Science Applications
Computer Networks and Communications
Artificial Intelligence

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title = "An introduction to kernel-based learning algorithms",

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.",

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

author = "M{\"u}ller, {Klaus Robert} and Sebastian Mika and Gunnar R{\"a}tsch and Koji Tsuda and Bernhard Sch{\"o}lkopf",

note = "Funding Information: Manuscript received August 10, 2000; revised December 15, 2000. This work was supported by DFG (JA 379/9-1, MU 987/1-1), EU (IST-1999-14190-BLISS), and travel grants from DAAD, NSF, and EU (Neurocolt II). Copyright: Copyright 2008 Elsevier B.V., All rights reserved.",

year = "2001",

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doi = "10.1109/72.914517",

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AU - Rätsch, Gunnar

AU - Tsuda, Koji

AU - Schölkopf, Bernhard

N1 - Funding Information: Manuscript received August 10, 2000; revised December 15, 2000. This work was supported by DFG (JA 379/9-1, MU 987/1-1), EU (IST-1999-14190-BLISS), and travel grants from DAAD, NSF, and EU (Neurocolt II). Copyright: Copyright 2008 Elsevier B.V., All rights reserved.

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N2 - 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.

AB - 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.

KW - Boosting

KW - Fisher's discriminant

KW - Kernel PCA

KW - Kernel methods

KW - Mathematical programming machines

KW - Mercer kernels

KW - Principal component analysis (PCA)

KW - Single-class classification

KW - Support vector machines (SVMs)

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An introduction to kernel-based learning algorithms

Abstract

Keywords

ASJC Scopus subject areas

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