Presentation | 2001/2/1 Function approximation by on-line variational Bayes learning Shin Ishii, Masa-aki Sato, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We formerly proposed an on-line EM algorithm for the normalized Gaussian network model. Athough the algorithm conducts on-line model selection schemes based on probabilistic interpretation, ambiguity has remained in their criterion. This study intends to remove the ambiguity by using a Bayes learning method. We propose an on-line variational Bayes learning method, in which the Bayes learning for the NGnet is implemented as a similar algorithm to the on-line EM algorithm. When applied to a simple two-dimensional function approximation problem and eight-dimensional benchmark problems, our method exhibits good performance. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Variational Bayes learning / EM algorithm / On-line learning / Normalized Gaussian network / Model selection |
Paper # | NC2000-90 |
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Committee | NC |
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Conference Date | 2001/2/1(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) | Function approximation by on-line variational Bayes learning |
Sub Title (in English) | |
Keyword(1) | Variational Bayes learning |
Keyword(2) | EM algorithm |
Keyword(3) | On-line learning |
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 | 2001/2/1 |
Paper # | NC2000-90 |
Volume (vol) | vol.100 |
Number (no) | 617 |
Page | pp.pp.- |
#Pages | 8 |
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