Article,

An introduction to kernel-based learning algorithms

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Neural Networks, IEEE Transactions on, 12 (2): 181 -201 (March 2001)
DOI: 10.1109/72.914517

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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