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  On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion

Smola, A., & Schölkopf, B. (1998). On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion. Algorithmica, 22(1-2), 211-231. doi:10.1007/PL00013831.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E7F5-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-C772-A
Genre: Journal Article

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 Creators:
Smola, AJ, Author              
Schölkopf, B1, 2, Author              
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We present a kernel-based framework for pattern recognition, regression estimation, function approximation, and multiple operator inversion. Adopting a regularization-theoretic framework, the above are formulated as constrained optimization problems. Previous approaches such as ridge regression, support vector methods, and regularization networks are included as special cases. We show connections between the cost function and some properties up to now believed to apply to support vector machines only. For appropriately chosen cost functions, the optimal solution of all the problems described above can be found by solving a simple quadratic programming problem.

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 Dates: 1998-09
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.1007/PL00013831
BibTex Citekey: 948
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Title: Algorithmica
Source Genre: Journal
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Publ. Info: New York : Springer-Verlag
Pages: - Volume / Issue: 22 (1-2) Sequence Number: - Start / End Page: 211 - 231 Identifier: ISSN: 0178-4617
CoNE: https://pure.mpg.de/cone/journals/resource/954925487793