Presentation | 2004-06-21 Accelerating Boltzmann machine Daisuke Itoh, Keita Torii, Tomo Munehisa, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Learning in a Boltzmann machine requires a very long calculation time because of statistical averaging. In order to reduce the time, the following improvements are performed on two steps where statistical averaging is taken in previous methods. At the first step, values of input units, output units and added hidden units are fixed so that they are separated linearly. By this separation, the necessity of taking statistics in the fixed state of input units and output units is lost. At the next step, statistical averaging in the state of fixed input units, freed output units and hidden units is approximated by the mean field theory. By this analytical technique, the calculation time can be shortened sharply. We show these two techniques in the n-bit-parity problem. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Boltzmann machine / linear separation / hidden unit / mean field theory |
Paper # | AI2004-11 |
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Conference Information | |
Committee | AI |
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Conference Date | 2004/6/14(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Artificial Intelligence and Knowledge-Based Processing (AI) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Accelerating Boltzmann machine |
Sub Title (in English) | |
Keyword(1) | Boltzmann machine |
Keyword(2) | linear separation |
Keyword(3) | hidden unit |
Keyword(4) | mean field theory |
1st Author's Name | Daisuke Itoh |
1st Author's Affiliation | Faculty of Engineering, University of Yamanashi() |
2nd Author's Name | Keita Torii |
2nd Author's Affiliation | Faculty of Engineering, University of Yamanashi |
3rd Author's Name | Tomo Munehisa |
3rd Author's Affiliation | Faculty of Engineering, University of Yamanashi |
Date | 2004-06-21 |
Paper # | AI2004-11 |
Volume (vol) | vol.104 |
Number (no) | 133 |
Page | pp.pp.- |
#Pages | 6 |
Date of Issue |