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  From regularization operators to support vector kernels

Smola, A., & Schölkopf, B. (1998). From regularization operators to support vector kernels. In M. Jordan, M. Kearns, & S. Solla (Eds.), Advances in Neural Information Processing Systems 10 (pp. 343-349). Cambridge, MA, USA: MIT Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E86D-F Version Permalink: http://hdl.handle.net/21.11116/0000-0005-DC24-B
Genre: Conference Paper

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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 derive the correspondence between regularization operators used in Regularization Networks and Hilbert Schmidt Kernels appearing in Sup-port Vector Machines. More specifica1ly, we prove that the Green's Func-tions associated with regularization operators are suitable Support Vector Kernels with equivalent regularization properties. As a by-product we show that a large number of Radial Basis Functions namely condition-ally positive definite functions may be used as Support Vector kernels.

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 Dates: 1998-06
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 797
 Degree: -

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Title: Eleventh Annual Conference on Neural Information Processing (NIPS 1997)
Place of Event: Denver, CO, USA
Start-/End Date: 1997-12-01 - 1997-12-06

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Source 1

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Title: Advances in Neural Information Processing Systems 10
Source Genre: Proceedings
 Creator(s):
Jordan, MI, Editor
Kearns, MJ, Editor
Solla, SA, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: 1089 Volume / Issue: - Sequence Number: - Start / End Page: 343 - 349 Identifier: ISBN: 0-262-10076-2

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Title: A Bradford Book
Source Genre: Series
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -