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  Choosing Multiple Parameters for Support Vector Machines

Chapelle, O., Vapnik V, Bousquet, O., & Mukherjee, S. (2002). Choosing Multiple Parameters for Support Vector Machines. Machine Learning, 46(1), 131-159.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E090-1 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E091-0
Genre: Journal Article

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 Creators:
Chapelle, O1, Author              
Vapnik V, Bousquet, O1, Author              
Mukherjee, S, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, escidoc:1497795              

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 Abstract: The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVM) is considered. This is done by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters. Usual methods for choosing parameters, based on exhaustive search become intractable as soon as the number of parameters exceeds two. Some experimental results assess the feasibility of our approach for a large number of parameters (more than 100) and demonstrate an improvement of generalization performance.

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 Dates: 2002
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: 1436
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Title: Machine Learning
Source Genre: Journal
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Pages: - Volume / Issue: 46 (1) Sequence Number: - Start / End Page: 131 - 159 Identifier: -