An introduction to kernel-based learning algorithms

Author

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

Author_Institution

GMD FIRST, Berlin, Germany

Volume

12

Issue

2

fYear

2001

fDate

3/1/2001 12:00:00 AM

Firstpage

181

Lastpage

201

Abstract

This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis 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 discussing applications such as optical character recognition and DNA analysis

Keywords

learning (artificial intelligence); learning automata; neural nets; optical character recognition; pattern classification; principal component analysis; DNA analysis; Mercer kernel; Vapnik-Chervonenkis theory; kernel Fisher discriminant analysis; learning algorithms; mathematical programming; optical character recognition; principal component analysis; support vector machines; Algorithm design and analysis; Character recognition; DNA; Kernel; Optical character recognition software; Pattern analysis; Principal component analysis; Support vector machine classification; Support vector machines; Time series analysis;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.914517

Filename

914517