Presentation | 1998/3/20 Designing Regularizers by Minimizing Generalization Errors Kazuhiro Yoshida, Masumi Ishikawa, Shun-ichi Amari, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | To improve generalization ability, a regularizer is frequently used. A novel approach proposed here is to regard the estimate of model parameters as a function of those without a regularizer. By minimizing the calcurated generalization error, the optimal function parameters and model parameters can be obtained. In this paper linear regression is adopted to carry out theoretical computation of generalization errors. It also contributes to the design of a new regularizer. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | generalization error / regularization / neural network / Gaussian regularizer / Laplace regularizer |
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Committee | NC |
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Conference Date | 1998/3/20(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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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) | Designing Regularizers by Minimizing Generalization Errors |
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Keyword(1) | generalization error |
Keyword(2) | regularization |
Keyword(3) | neural network |
Keyword(4) | Gaussian regularizer |
Keyword(5) | Laplace regularizer |
1st Author's Name | Kazuhiro Yoshida |
1st Author's Affiliation | Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology() |
2nd Author's Name | Masumi Ishikawa |
2nd Author's Affiliation | Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology |
3rd Author's Name | Shun-ichi Amari |
3rd Author's Affiliation | RIKEN |
Date | 1998/3/20 |
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Volume (vol) | vol.97 |
Number (no) | 624 |
Page | pp.pp.- |
#Pages | 8 |
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