| Presentation | 2012-11-07 Model Selection of Bernoulli Mixture in Variational Bayes Learning Kazumi DAKUJAKU, Sumio WATANABE, |
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| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Variational Bayes learning, which is defined by the mean field approximation of the joint posterior distribution of a parameter and a hidden variable, approximates the Bayes posterior distribution with a low computational cost. In Bayes learning, it was proved that WAIC is an asymptotic unbiased estimator of the generalization loss, whereas in variational Bayes learning such an information criterion is still unknown. Recently a new method WAIC-VB was proposed which approximates the Bayes generalization loss using the importance sampling method based on the variational Bayes. In the present paper, we propose that WAIC-VB is useful in the model selection problem of a Bernoulli mixture, which is shown by experimental results in both artificial and practical applications. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | variational Bayes method / cross validation / Gaussian mixture / hyperparameter |
| Paper # | IBISML2012-39 |
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| Committee | IBISML |
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| Conference Date | 2012/10/31(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Model Selection of Bernoulli Mixture in Variational Bayes Learning |
| Sub Title (in English) | |
| Keyword(1) | variational Bayes method |
| Keyword(2) | cross validation |
| Keyword(3) | Gaussian mixture |
| Keyword(4) | hyperparameter |
| 1st Author's Name | Kazumi DAKUJAKU |
| 1st Author's Affiliation | Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology() |
| 2nd Author's Name | Sumio WATANABE |
| 2nd Author's Affiliation | Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology |
| Date | 2012-11-07 |
| Paper # | IBISML2012-39 |
| Volume (vol) | vol.112 |
| Number (no) | 279 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |