An introduction to kernel-based learning algorithms

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...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on neural networks Vol. 12; no. 2; pp. 181 - 201
Main Authors Muller, K.-R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.03.2001
Subjects
Algorithm design and analysis
Algorithms
Character recognition
Deoxyribonucleic acid
Discriminant analysis
DNA
Kernel
Kernels
Learning
Neural networks
Optical character recognition
Optical character recognition software
Pattern analysis
Principal component analysis
Support vector machine classification
Support vector machines
Time series analysis
Online AccessGet full text
ISSN1045-9227
DOI10.1109/72.914517

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ObjectType-Article-1
ObjectType-Feature-2
ISSN:1045-9227
DOI:10.1109/72.914517