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  Model Selection for Support Vector Machines

Chapelle, O., & Vapnik, V. (2000). Model Selection for Support Vector Machines. In S. Solla, T. Leen, & K. Müller (Eds.), Advances in Neural Information Processing Systems 12 (pp. 230-236). Cambridge, MA, USA: MIT Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E4C6-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-B5B0-7
Genre: Conference Paper

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 Creators:
Chapelle, O, Author              
Vapnik, V1, Author              
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: New functionals for parameter (model) selection of Support Vector Machines are introduced based on the concepts of the span of support vectors and rescaling of the feature space. It is shown that using these functionals, one can both predict the best choice of parameters of the model and the relative quality of performance for any value of parameter.

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 Dates: 2000-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2161
 Degree: -

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Title: Thirteenth Annual Neural Information Processing Systems Conference (NIPS 1999)
Place of Event: Denver, CO, USA
Start-/End Date: 2000-11-29 - 2000-12-04

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Title: Advances in Neural Information Processing Systems 12
Source Genre: Proceedings
 Creator(s):
Solla, SA, Editor
Leen, TK, Editor
Müller, K, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 230 - 236 Identifier: ISBN: 0-262-19450-3