Presentation | 1996/3/18 Bayesian Estimation of Parameters of Various Regularizers in the Learning of Neural Networks Kazuhiro Yoshida, Masumi Ishikawa, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | A regularizer is often used in the learning of neural networks to avoid over-generalization or over-fitting. It is effective, because it penalizes large connection weights. In most cases, a regularizer of the sum of squared connection weights, called a Gaussian regularizer, is used. It corresponds to the one proposed by Plant and others. One of th difficulties in the use of regularizers is that the determibation of a regularization parameter. The present paper proposes the use of various types of regularizers such as the sum of the absolute values of connection weight, called a Laplace regularizer, which has been used in structural learning with fogetting. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Bayesian estimation / regularization / neural network |
Paper # | NC-95-122 |
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Committee | NC |
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Conference Date | 1996/3/18(1days) |
Place (in Japanese) | (See Japanese page) |
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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) | Bayesian Estimation of Parameters of Various Regularizers in the Learning of Neural Networks |
Sub Title (in English) | |
Keyword(1) | Bayesian estimation |
Keyword(2) | regularization |
Keyword(3) | neural network |
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 |
Date | 1996/3/18 |
Paper # | NC-95-122 |
Volume (vol) | vol.95 |
Number (no) | 598 |
Page | pp.pp.- |
#Pages | 8 |
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