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| Presentation | 2017-11-10 13:00 Approximated hyperparameter distribution estimation using Gaussian process and Bayesian optimization Shun Katakami, Hirotaka Sakamoto, Masato Okada (UTokyo)  IBISML2017-81 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In order to reduce the computational cost of Bayesian inference, we propose a method to estimate the Bayesian posterior probability distribution and its maximum value approximately from a small number of distribution sampling using Gaussian process and Bayesian optimization. In this study, we aim to estimate hyperparameter distribution of Markov random field (MRF) model used for image processing. The hyperparameter distribution is interpolated from a small number of samples by Gaussian process. Also, The maximum value of the distribution is estimated by Bayesian optimization. By numerical experiments, we show that by using these methods, it is possible to estimate the distribution and its maximum value accurately while reducing the computational cost. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Gaussian process / Bayesian inference / Bayesian optimization / Markov random field / hyperparameter distribution estimation / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 117, no. 293, IBISML2017-81, pp. 333-338, Nov. 2017. | |
| Paper # | IBISML2017-81 | |
| Date of Issue | 2017-11-02 (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 | IBISML2017-81 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2017-11-08 - 2017-11-10 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Univ. of Tokyo |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Information-Based Induction Science Workshop (IBIS2017) |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2017-11-IBISML |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Approximated hyperparameter distribution estimation using Gaussian process and Bayesian optimization |
| Sub Title (in English) | |
| Keyword(1) | Gaussian process |
| Keyword(2) | Bayesian inference |
| Keyword(3) | Bayesian optimization |
| Keyword(4) | Markov random field |
| Keyword(5) | hyperparameter distribution estimation |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Shun Katakami |
| 1st Author's Affiliation | The University of Tokyo (UTokyo) |
| 2nd Author's Name | Hirotaka Sakamoto |
| 2nd Author's Affiliation | The University of Tokyo (UTokyo) |
| 3rd Author's Name | Masato Okada |
| 3rd Author's Affiliation | The University of Tokyo (UTokyo) |
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| Speaker | Author-1 |
| Date Time | 2017-11-10 13:00:00 |
| Presentation Time | 150 minutes |
| Registration for | IBISML |
| Paper # | IBISML2017-81 |
| Volume (vol) | vol.117 |
| Number (no) | no.293 |
| Page | pp.333-338 |
| #Pages | 6 |
| Date of Issue | 2017-11-02 (IBISML) |
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