Presentation | 2007-03-14 Progressive Contrastive Divergence Method Kazuya TAKABATAKE, Shotaro AKAHO, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In cases that a gradient method is used for maximum likelihood estimation of a multivariate probability model, evaluation of the gradient vector often requires massive computation. Contrastive divergence method uses some approximation and a Monte Carlo method to reduce the computation for the evaluation of the gradient vector. Due to this approximation, the results from contrastive divergence method differs from the true maximum likelihood estimation. In this paper, progressive contrastive divergence method that is a modification of contrastive divergence method is shown. The result from progressive divergence method theoretically coincide with the true maximum likelihood estimation. Results of experiments that use Boltzmann machine for the probability model are shown. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | contrastive divergence method / Monte Carlo method / Markov chain Monte Carlo / Maximum likelihood estimation / convergence |
Paper # | NC2006-143 |
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Committee | NC |
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Conference Date | 2007/3/7(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) | Progressive Contrastive Divergence Method |
Sub Title (in English) | |
Keyword(1) | contrastive divergence method |
Keyword(2) | Monte Carlo method |
Keyword(3) | Markov chain Monte Carlo |
Keyword(4) | Maximum likelihood estimation |
Keyword(5) | convergence |
1st Author's Name | Kazuya TAKABATAKE |
1st Author's Affiliation | Neuroscience Research Institute, AIST() |
2nd Author's Name | Shotaro AKAHO |
2nd Author's Affiliation | Neuroscience Research Institute, AIST |
Date | 2007-03-14 |
Paper # | NC2006-143 |
Volume (vol) | vol.106 |
Number (no) | 588 |
Page | pp.pp.- |
#Pages | 4 |
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