| Presentation | 2003/12/1 Accuracy of the MCMC Methods in Learning Models with Singularities Katsuyuki TAKAHASHl, Sumio WATANABE, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In the Bayesian learning, the stochastic complexity has been used for model selection arid optimization of hyper parameters. However, since there has been no method to calculate analytically the stochastic complexity, we could not evaluate how precise the MCMC method is. In this paper, we apply the algebraic geometrical method to the stochastic complexity of singular learning machines, and clarify the properties of the MCMC method. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | stochastic complexity / MCMC method / regular models / learning models with singularity / algebraic |
| Paper # | NC2003-103 |
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| Committee | NC |
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| Conference Date | 2003/12/1(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 | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Accuracy of the MCMC Methods in Learning Models with Singularities |
| Sub Title (in English) | |
| Keyword(1) | stochastic complexity |
| Keyword(2) | MCMC method |
| Keyword(3) | regular models |
| Keyword(4) | learning models with singularity |
| Keyword(5) | algebraic |
| 1st Author's Name | Katsuyuki TAKAHASHl |
| 1st Author's Affiliation | Department of Advanced Applied Electronics Tokyo Institute of Technology() |
| 2nd Author's Name | Sumio WATANABE |
| 2nd Author's Affiliation | P&I Lab.Tokyo Institute |
| Date | 2003/12/1 |
| Paper # | NC2003-103 |
| Volume (vol) | vol.103 |
| Number (no) | 490 |
| Page | pp.pp.- |
| #Pages | 6 |
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