Presentation 1998/3/20
Designing Regularizers by Minimizing Generalization Errors
Kazuhiro Yoshida, Masumi Ishikawa, Shun-ichi Amari,
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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.
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Keyword(in English) generalization error / regularization / neural network / Gaussian regularizer / Laplace regularizer
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Committee NC
Conference Date 1998/3/20(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Designing Regularizers by Minimizing Generalization Errors
Sub Title (in English)
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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