Presentation | 2003/11/15 Research of Generalization Ability for Local Lenearized Least Squares Algorithm Masahiro YOSHIDA, Hiroshi NINOMIYA, Hideki ASAI, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | A variety of studies on training method with the weight decay has been done in order to improve the generalization ability in feedforward neural networks. In this research, we introduce the local training with weight decay based on the recursive least squares. The proposed method can improve the training speed. In the simulation, the generalization ability is compared the proposed method to another methods with weight decay. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | feedforward neural networks / local training / weight decay / recursive least squares / generalization ability |
Paper # | NLP2003-123 |
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Conference Information | |
Committee | NLP |
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Conference Date | 2003/11/15(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Nonlinear Problems (NLP) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Research of Generalization Ability for Local Lenearized Least Squares Algorithm |
Sub Title (in English) | |
Keyword(1) | feedforward neural networks |
Keyword(2) | local training |
Keyword(3) | weight decay |
Keyword(4) | recursive least squares |
Keyword(5) | generalization ability |
1st Author's Name | Masahiro YOSHIDA |
1st Author's Affiliation | Dept. of Systems Engineering, Shizuoka University() |
2nd Author's Name | Hiroshi NINOMIYA |
2nd Author's Affiliation | Dept. of Information Science, Shonan Institute of Technology |
3rd Author's Name | Hideki ASAI |
3rd Author's Affiliation | Dept. of Systems Engineering, Shizuoka University |
Date | 2003/11/15 |
Paper # | NLP2003-123 |
Volume (vol) | vol.103 |
Number (no) | 464 |
Page | pp.pp.- |
#Pages | 6 |
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