An introduction to kernel-based learning algorithms
Muller, K.-R.; Mika, S.; Ratsch, G.; Tsuda, K.; Scholkopf, B.
Neural Networks, IEEE Transactions on
Volume 12, Issue 2, Mar 2001 Page(s):181 - 201
Digital Object Identifier   10.1109/72.914517
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

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