Presentation | 2002/1/22 A variational Bayes learning for normalized Gaussian network Shin Ishii, Masa-aki Sato, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Normalized Gaussian network, which is a function approximator, can be formulated as a mixture model of exponential distributions. Its inference can then be done by the variational Bayes learning, which is a natural extension of the expectation-maximization (EM) algorithm for the maximum likelihood inference. This study introduces a hierarchical prior distribution and a confidence parameter to the variational Bayes learning. In addition, a hierarchical model selection heuristics is introduced. When applied to a simple two-dimensional function approximation problem and a reconstruction problem of a chaotic dynamical system, the modified VB method achieves good results. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Bayes learning / Variational Bayes learning / EM algorithm / Normalized Gaussian network / Model selection |
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Committee | NC |
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Conference Date | 2002/1/22(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) | A variational Bayes learning for normalized Gaussian network |
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Keyword(1) | Bayes learning |
Keyword(2) | Variational Bayes learning |
Keyword(3) | EM algorithm |
Keyword(4) | Normalized Gaussian network |
Keyword(5) | Model selection |
1st Author's Name | Shin Ishii |
1st Author's Affiliation | Nara Institute of Science and Technology:CREST Doya Project, Japan Science and Technology Corporation() |
2nd Author's Name | Masa-aki Sato |
2nd Author's Affiliation | Advanced Telecommunication Research Institute International (ATR):CREST Doya Project, Japan Science and Technology Corporation |
Date | 2002/1/22 |
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Volume (vol) | vol.101 |
Number (no) | 616 |
Page | pp.pp.- |
#Pages | 8 |
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