Zusammenfassung
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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