Paper Abstract and Keywords |
Presentation |
2017-11-09 13:00
[Poster Presentation]
Learning huge Bayesian networks using RAI algorithm based on Bayes factor Kazuki Natori, Masaki Uto, Maomi Ueno (UEC) IBISML2017-58 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
``Learning Bayesian networks'' has NP-hard problem. The state-of-the-arts method of learning Bayesian networks cannot learn structures that have more than 60 variables. For the reasons, we proposed the method that reduces computational cost dynamically. Concretely, this method provides a conditional independence (CI) test using Bayes factor that has asymptotic consistency to the RAI algorithm. In the experiments, our proposed method can learn the structure that has more than 1000 variables. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Learning Bayesian networks / Bayes factor / Hypothesis testing / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 117, no. 293, IBISML2017-58, pp. 177-184, Nov. 2017. |
Paper # |
IBISML2017-58 |
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) |
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IBISML2017-58 |
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) |
Learning huge Bayesian networks using RAI algorithm based on Bayes factor |
Sub Title (in English) |
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Keyword(1) |
Learning Bayesian networks |
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Bayes factor |
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Hypothesis testing |
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1st Author's Name |
Kazuki Natori |
1st Author's Affiliation |
The University of Electro-Communications (UEC) |
2nd Author's Name |
Masaki Uto |
2nd Author's Affiliation |
The University of Electro-Communications (UEC) |
3rd Author's Name |
Maomi Ueno |
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The University of Electro-Communications (UEC) |
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Speaker |
Author-1 |
Date Time |
2017-11-09 13:00:00 |
Presentation Time |
150 minutes |
Registration for |
IBISML |
Paper # |
IBISML2017-58 |
Volume (vol) |
vol.117 |
Number (no) |
no.293 |
Page |
pp.177-184 |
#Pages |
8 |
Date of Issue |
2017-11-02 (IBISML) |
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