Author(s): Mller KR, Mika S, Rtsch G, Tsuda K, Schlkopf B, Mller KR, Mika S, Rtsch G, Tsuda K, Schlkopf B
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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.
This article was published in IEEE Trans Neural Netw
and referenced in Journal of Thermodynamics & Catalysis