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  An Introduction to Kernel-Based Learning Algorithms

Müller, K.-R., Mika, S., Rätsch, G., Tsuda, K., & Schölkopf, B. (2001). An Introduction to Kernel-Based Learning Algorithms. IEEE Transactions on Neural Networks, 12(2), 181-201. doi:10.1109/72.914517.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E2C4-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-8E01-A
Genre: Journal Article

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Müller, K-R, Author              
Mika, S, Author
Rätsch, G, Author              
Tsuda, K, Author              
Schölkopf, B1, Author              
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1External Organizations, ou_persistent22              

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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

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 Dates: 2001-03
 Publication Status: Published in print
 Pages: -
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 Rev. Method: -
 Identifiers: DOI: 10.1109/72.914517
BibTex Citekey: 1876
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Title: IEEE Transactions on Neural Networks
Source Genre: Journal
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Publ. Info: New York, NY : Institute of Electrical and Electronics Engineers
Pages: - Volume / Issue: 12 (2) Sequence Number: - Start / End Page: 181 - 201 Identifier: ISSN: 1045-9227
CoNE: https://pure.mpg.de/cone/journals/resource/954925591430