Presentation | 2009-01-19 On the Effect of Hyperparamater to Generalization Error in Variational Bayes Learning Shinji OYAMA, Sumio WATANABE, |
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Abstract(in English) | In variational Bayes learning, the probability distribution of the hidden variable and parameter is made by the mean field approximation. In a mixture of exponential probability distributions such a normal mixture, the mean field approximation is applied to wide practical problems because it provides the fast recursive learning algorithm. It was theoretically clarified the phase transition structure of variational Bayes learning for hyperparameter. In this paper, we study the generalization errors of variational Bayes learning and experimentally show the following facts. (1)The behavior of the generalization error changes between the phase transition point. (2)At an ordinary point, the generalization error strongly depends on the condition that the true distribution is contained in the statistical model or not, whereas, at the critical point, the generalization error is not so depend on the condition. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Singular learning machines / variational Bayes generalization error / Gibbs generalization error / hyperparameter |
Paper # | NC2008-87 |
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Committee | NC |
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Conference Date | 2009/1/12(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) | On the Effect of Hyperparamater to Generalization Error in Variational Bayes Learning |
Sub Title (in English) | |
Keyword(1) | Singular learning machines |
Keyword(2) | variational Bayes generalization error |
Keyword(3) | Gibbs generalization error |
Keyword(4) | hyperparameter |
1st Author's Name | Shinji OYAMA |
1st Author's Affiliation | Department of Computer Science, Tokyo Institute of Technology() |
2nd Author's Name | Sumio WATANABE |
2nd Author's Affiliation | Precision and Intelligence Laboratory, Tokyo Institute of Technology |
Date | 2009-01-19 |
Paper # | NC2008-87 |
Volume (vol) | vol.108 |
Number (no) | 383 |
Page | pp.pp.- |
#Pages | 6 |
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