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  Kernel Methods in Machine Learning

Hofmann, T., Schölkopf, B., & Smola, A. (2008). Kernel Methods in Machine Learning. The Annals of Statistics, 36(3), 1171-1220. doi:10.1214/009053607000000677.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C8CF-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-304E-0
Genre: Journal Article

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 Creators:
Hofmann, T1, 2, Author              
Schölkopf, B1, 2, Author              
Smola, AJ, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions. The latter include nonlinear functions as well as functions defined on nonvectorial data.

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 Dates: 2008-06
 Publication Status: Published in print
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1214/009053607000000677
BibTex Citekey: 4268
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Title: The Annals of Statistics
Source Genre: Journal
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Publ. Info: Cleveland, Ohio [etc] : Institute of Mathematical Statistics [etc.]
Pages: - Volume / Issue: 36 (3) Sequence Number: - Start / End Page: 1171 - 1220 Identifier: ISSN: 0090-5364
CoNE: https://pure.mpg.de/cone/journals/resource/954925461135