Presentation | 2006-03-15 A mean field algorithm for Bayesian learning in perceptron type probabilistic models Shinsuke UDA, Yoshiyuki KABASHIMA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We develop a computationally tractable approximate algorithm for Bayesian learning in perceptron type probabilistic models. The algorithm is designed to efficiently solve the adaptive TAP equations, recently introduced by Opper and Winther, based on message passing scheme. Unlike the conventional method, the adaptive TAP scheme adaptively approximates the Bayesian inference without knowledge of the generative model of given data, which is preferred in analysis of real world data. The obtained algorithm is applied to the two tasks; (a) a classification problem using real world data and (b) multiuser detection for code division multiple access (CDMA) communication. Numerical experiments for the two tasks indicate the efficacy of the developed algorithm. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Adaptive TAP mean field method / Bayesian learning / Belief propagation / Classification / CDMA communication |
Paper # | NC2005-114 |
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Committee | NC |
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Conference Date | 2006/3/8(1days) |
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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) | A mean field algorithm for Bayesian learning in perceptron type probabilistic models |
Sub Title (in English) | |
Keyword(1) | Adaptive TAP mean field method |
Keyword(2) | Bayesian learning |
Keyword(3) | Belief propagation |
Keyword(4) | Classification |
Keyword(5) | CDMA communication |
1st Author's Name | Shinsuke UDA |
1st Author's Affiliation | Graduate School of Science and Engineering, Tokyo Institute of Technology() |
2nd Author's Name | Yoshiyuki KABASHIMA |
2nd Author's Affiliation | Graduate School of Science and Engineering, Tokyo Institute of Technology |
Date | 2006-03-15 |
Paper # | NC2005-114 |
Volume (vol) | vol.105 |
Number (no) | 657 |
Page | pp.pp.- |
#Pages | 6 |
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