Presentation	2007-12-22 Optimizing SVR Hyperparameters via Fast Cross-Validation using AOSVR Masayuki KARASUYAMA, Ryohei NAKANO,
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Abstract(in English)	The performance of Support Vector Regression (SVR) deeply depends on its hyperparameters. such as an insensitive zone thickness, a penalty factor, and kernel parameters. A method called MCV-SVR was once proposed, which optimizes SVR hyperparameters so that cross-validation error is minimized. However, the computational cost of CV is usually high. In this paper we apply Accurate Online Support Vector Regression (AOSVR) to the MCV-SVR cross-validation procedure. The AOSVR enables an efficient update of a trained SVR function. We show the AOSVR dramatically accelerates the MCV-SVR. Moreover, our experiments showed our faster MCV-SVR has better generalization than other existing methods.
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Keyword(in English)	support vector machines / support vector regression / mimum cross-validation
Paper #	NC2007-73
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Committee	NC
Conference Date	2007/12/15(1days)
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Paper Information
Registration To	Neurocomputing (NC)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Optimizing SVR Hyperparameters via Fast Cross-Validation using AOSVR
Sub Title (in English)
Keyword(1)	support vector machines
Keyword(2)	support vector regression
Keyword(3)	mimum cross-validation
1st Author's Name	Masayuki KARASUYAMA
1st Author's Affiliation	Nagoya Institute of Technology()
2nd Author's Name	Ryohei NAKANO
2nd Author's Affiliation	Nagoya Institute of Technology
Date	2007-12-22
Paper #	NC2007-73
Volume (vol)	vol.107
Number (no)	410
Page	pp.pp.-
#Pages	6
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