| Presentation | 2010-01-18 Necessary Data Length for HMMs Based on the Vicarious Bayes Learning Keisuke YAMAZAKI, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The present paper analyzes the change of parameter learning due to feature selections and investigates conditions to have the same learning result in both original and feature spaces. Moreover, we propose a fast and precise learning method referred to as the vicarious Bayes learning. We also apply it to hidden Markov model and derive a necessary length for the complete parameter learning. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Feature Selection / Dimension Reduction / Bayes Learning / Algebraic Geometry |
| Paper # | NC2009-77 |
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| Conference Information | |
| Committee | NC |
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| Conference Date | 2010/1/11(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) | Necessary Data Length for HMMs Based on the Vicarious Bayes Learning |
| Sub Title (in English) | |
| Keyword(1) | Feature Selection |
| Keyword(2) | Dimension Reduction |
| Keyword(3) | Bayes Learning |
| Keyword(4) | Algebraic Geometry |
| 1st Author's Name | Keisuke YAMAZAKI |
| 1st Author's Affiliation | P& I Laboratory, Tokyo Institute of Technology() |
| Date | 2010-01-18 |
| Paper # | NC2009-77 |
| Volume (vol) | vol.109 |
| Number (no) | 363 |
| Page | pp.pp.- |
| #Pages | 6 |
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