An introduction to kernel-based learning algorithms
Muller, K.-R.
Mika, S.
Ratsch, G.
Tsuda, K.
Scholkopf, B.
GMD FIRST, Berlin;
This paper appears in: Neural Networks, IEEE Transactions on
Publication Date: Mar 2001
Volume: 12,
Issue: 2
On page(s): 181-201
ISSN: 1045-9227
References Cited: 155
CODEN: ITNNEP
INSPEC Accession Number: 6898055
DOI: 10.1109/72.914517
Posted online: 2002-08-07 00:20:16.0
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
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