Presentation | 2002/12/6 Optimizing Hyper-parameters for Support Vector Regression Kentaro ITO, Ryohei NAKANO, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Support Vector Machines / Support Vector Regression / Cross-Validation |
Paper # | NC2002-89 |
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Committee | NC |
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Conference Date | 2002/12/6(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Neurocomputing (NC) |
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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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