| 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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| Conference Information | |
| Committee | NC |
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| Conference Date | 2002/12/6(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
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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 |
| Date of Issue | |