Presentation | 2003/9/22 Local Linearized Least Squares Algorithm based on Penalty Function Method Masahiro YOSHIDA, Hiroshi NINOMIYA, Hideki ASAI, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In this research, we introduce the local training method for feedforward neural networks. The local trainingmethods improve the computational complexity compared to the global training methods. Our method also can improve thecomputational complexity, because of learning for each neuron in neural networks. In addition, the proposed training method yields results with high convergence rates by using penalty term which is derived from Hessian matrix of the cost functionwith respect to weight. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Feedforward Neural Networks / Local Training / Penalty Function / Recursive Least Squares |
Paper # | MLP2003-63 |
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Committee | NLP |
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Conference Date | 2003/9/22(1days) |
Place (in Japanese) | (See Japanese page) |
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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) | Local Linearized Least Squares Algorithm based on Penalty Function Method |
Sub Title (in English) | |
Keyword(1) | Feedforward Neural Networks |
Keyword(2) | Local Training |
Keyword(3) | Penalty Function |
Keyword(4) | Recursive Least Squares |
1st Author's Name | Masahiro YOSHIDA |
1st Author's Affiliation | Department of Systems Engineering, Shizuoka University() |
2nd Author's Name | Hiroshi NINOMIYA |
2nd Author's Affiliation | Dept. of Inf. Science, Shonan Institute of Technology |
3rd Author's Name | Hideki ASAI |
3rd Author's Affiliation | Department of Systems Engineering, Shizuoka University |
Date | 2003/9/22 |
Paper # | MLP2003-63 |
Volume (vol) | vol.103 |
Number (no) | 335 |
Page | pp.pp.- |
#Pages | 6 |
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