| Presentation | 2006-03-15 Exchange Monte Carlo Method for Bayesian Lerning of Singular Learning Machines Kenji NAGATA, Sumio WATANABE, |
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| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | A lot of singular learning machines such as neural networks, normal mixtures, Bayesian networks and hidden Markov models are widely used in practical information systems. In these learning machines, it was clarified that the Bayesian learning provides the better generalization performance than the maximum likelihood method. However, it needs huge computational cost to realize the Bayesian posterior distribution by the conventional Monte Carlo method. In this paper, we propose that the exchange Monte Carlo method is appropriate for the Bayesian learning of singular learning machines, and experimentally show that it attains the better posterior distribution than the conventional Monte Carlo method. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Exchange Monte Carlo Method / Singular Learning Machines / Bayesian Posterior Distribution |
| Paper # | NC2005-118 |
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| Committee | NC |
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| Conference Date | 2006/3/8(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Neurocomputing (NC) |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Exchange Monte Carlo Method for Bayesian Lerning of Singular Learning Machines |
| Sub Title (in English) | |
| Keyword(1) | Exchange Monte Carlo Method |
| Keyword(2) | Singular Learning Machines |
| Keyword(3) | Bayesian Posterior Distribution |
| 1st Author's Name | Kenji NAGATA |
| 1st Author's Affiliation | Department of Computer Science Tokyo Institute of Technology() |
| 2nd Author's Name | Sumio WATANABE |
| 2nd Author's Affiliation | PI Lab., Tokyo Institute of Technology. |
| Date | 2006-03-15 |
| Paper # | NC2005-118 |
| Volume (vol) | vol.105 |
| Number (no) | 657 |
| Page | pp.pp.- |
| #Pages | 6 |
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