Presentation | 2008-03-13 A Statistical Analysis of Support Vector Machines of Forgetting Factor Yoshihiko NOMURA, Hiroyuki FUNAYA, Kazushi IKEDA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Support Vector Machines (SVMs) are, in general, trained in batch but any trick is necessary when the target to be trained is time-varying. In this study, we introduced the idea of forgetting factor (FF) of the RLS algorithm for adaptive filters and analyzed its effect on the SVM performance. More concretely, we derived the average generalization error of the algorithm in a simple case where input space is one-dimensional. The average generalization error of the SVMs with FF does not converge to zero, differently from the SVM in batch or online. We confirmed our results by computer simulations. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | support vector machine / forgetting factor / average generalization error |
Paper # | NC2007-168 |
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Committee | NC |
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Conference Date | 2008/3/5(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | A Statistical Analysis of Support Vector Machines of Forgetting Factor |
Sub Title (in English) | |
Keyword(1) | support vector machine |
Keyword(2) | forgetting factor |
Keyword(3) | average generalization error |
1st Author's Name | Yoshihiko NOMURA |
1st Author's Affiliation | Department of Systems Science, Graduate School of Informatics, Kyoto-University() |
2nd Author's Name | Hiroyuki FUNAYA |
2nd Author's Affiliation | Department of Systems Science, Graduate School of Informatics, Kyoto-University |
3rd Author's Name | Kazushi IKEDA |
3rd Author's Affiliation | Department of Systems Science, Graduate School of Informatics, Kyoto-University |
Date | 2008-03-13 |
Paper # | NC2007-168 |
Volume (vol) | vol.107 |
Number (no) | 542 |
Page | pp.pp.- |
#Pages | 6 |
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