| Presentation | 2006-03-17 Stochastic Complexity of Complete Bibpartite Graph-type Boltzmann Machines in Mean Field Approximation Yu NISHIYAMA, Sumio WATANABE, |
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| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In the learning of singular learning machines, the superiority of Bayesian learning is shown. However, it requires huge computational costs to realize the Bayesian a posteriori distribution. To overcome this problem, the mean field approximation, which is originally known in statistical physics, is used in the practical information systems. Recently, the theoretical properties such as generalization error or free energy in the mean field approximation has been studied. The theoretical results give us the comparison with the regular statistical model and the foundation of a model selection. In this paper, we treat the complete bibpartite Boltzmann machines and derive the upper bound of asymptotic free energy of the mean field approximation. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Singular Learning Machines / Stochastic Complexity / Mean Field Approximation / Boltzmann Machines |
| Paper # | NC2005-172 |
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| Committee | NC |
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| Conference Date | 2006/3/10(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) | Stochastic Complexity of Complete Bibpartite Graph-type Boltzmann Machines in Mean Field Approximation |
| Sub Title (in English) | |
| Keyword(1) | Singular Learning Machines |
| Keyword(2) | Stochastic Complexity |
| Keyword(3) | Mean Field Approximation |
| Keyword(4) | Boltzmann Machines |
| 1st Author's Name | Yu NISHIYAMA |
| 1st Author's Affiliation | Department of Computational Intelligence and System Science, Tokyo Institute of Technology() |
| 2nd Author's Name | Sumio WATANABE |
| 2nd Author's Affiliation | Precision and Intelligence Laboratory. Tokyo Institute of Technology |
| Date | 2006-03-17 |
| Paper # | NC2005-172 |
| Volume (vol) | vol.105 |
| Number (no) | 659 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |