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- W2108995755 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." @default.
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- W2108995755 date "2001-03-01" @default.
- W2108995755 modified "2023-10-17" @default.
- W2108995755 title "An introduction to kernel-based learning algorithms" @default.
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- W2108995755 doi "https://doi.org/10.1109/72.914517" @default.
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