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| Presentation | 2012-03-12 11:50 Hyperparameter Selection of Infinite Gaussian Mixture Model via Widely Applicable Information Criterion Takushi Miki, Masahiro Kohjima, Sumio Watanabe (titech)  IBISML2011-91 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Recently, nonparametric Bayesian method is applied to wide range of research fields such as natural language processing, ob ject tracking, and recommendation system. Ma jor advantage of this method is that the complexity of the model need not to be explicitly decided and is implicitly controlled by hyperparameters. Although the choice of hyperparameters has a considerable effect to learning results and predictive distribution, the design of hyperparameters is not yet studied. In this paper, authors propose hyperparameter design method based on Widely Applicable Information Criterion(WAIC), which has a theoretical support in general parametric models. Experimental results show the effectiveness of proposed method. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | nonparametric Bayesian method / infinite gaussian mixture / hyperparameter selection / information criterion / widely applicable information criterion / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 111, no. 480, IBISML2011-91, pp. 29-33, March 2012. | |
| Paper # | IBISML2011-91 | |
| Date of Issue | 2012-03-05 (IBISML) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | IBISML2011-91 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2012-03-12 - 2012-03-13 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | The Institute of Statistical Mathematics |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Mathematical Statistics, Databases, etc. |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2012-03-IBISML |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Hyperparameter Selection of Infinite Gaussian Mixture Model via Widely Applicable Information Criterion |
| Sub Title (in English) | |
| Keyword(1) | nonparametric Bayesian method |
| Keyword(2) | infinite gaussian mixture |
| Keyword(3) | hyperparameter selection |
| Keyword(4) | information criterion |
| Keyword(5) | widely applicable information criterion |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Takushi Miki |
| 1st Author's Affiliation | Tokyo Institute of Technology (titech) |
| 2nd Author's Name | Masahiro Kohjima |
| 2nd Author's Affiliation | Tokyo Institute of Technology (titech) |
| 3rd Author's Name | Sumio Watanabe |
| 3rd Author's Affiliation | Tokyo Institute of Technology (titech) |
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| Speaker | Author-1 |
| Date Time | 2012-03-12 11:50:00 |
| Presentation Time | 25 minutes |
| Registration for | IBISML |
| Paper # | IBISML2011-91 |
| Volume (vol) | vol.111 |
| Number (no) | no.480 |
| Page | pp.29-33 |
| #Pages | 5 |
| Date of Issue | 2012-03-05 (IBISML) |
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