Presentation 2002/12/6
Optimizing Hyper-parameters for Support Vector Regression
Kentaro ITO, Ryohei NAKANO,
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Abstract(in English) This paper presents a method to optimize hyper parameters for Support Vector Regression(SVR) by using the Minimum Cross-Validation regularizer. The method finds the optimal set of hyper parameters to minimize the validation error of SVR by using a coordinate descent method.
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Keyword(in English) Support Vector Machines / Support Vector Regression / Cross-Validation
Paper # NC2002-89
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Conference Information
Committee NC
Conference Date 2002/12/6(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Optimizing Hyper-parameters for Support Vector Regression
Sub Title (in English)
Keyword(1) Support Vector Machines
Keyword(2) Support Vector Regression
Keyword(3) Cross-Validation
1st Author's Name Kentaro ITO
1st Author's Affiliation Department of Intelligence and Computer Science, Nagoya Institute of Technology()
2nd Author's Name Ryohei NAKANO
2nd Author's Affiliation Department of Intelligence and Computer Science, Nagoya Institute of Technology
Date 2002/12/6
Paper # NC2002-89
Volume (vol) vol.102
Number (no) 508
Page pp.pp.-
#Pages 6
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