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| Presentation | 2017-11-09 13:00 [Poster Presentation] Empirical Bayesian Tree Masashi Sekino (SMN)  IBISML2017-39 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | We propose a new decision tree learning algorithm ``Empirical Bayesian Tree (EBT)'', which models the outputs of a leaf node by a probability distribution and uses the marginal likelihood for a criterion to construct a decision tree. When we use the exponential family distributions for modeling the outputs, the marginal likelihood can be efficiently calculated by updating sufficient statistics. By using marginal likelihood, EBT can prune the bisection tree or construct a multi-split tree by splitting a continuous explanatory variable into multiple intervals. In the EBT framework, not only the Bernoulli distribution for a discriminant task or the Normal distribution for a least squares regression problem, but also the Poisson distribution or the Exponential distribution can be used. We also examined the Random Forest using EBTs ``Random Empirical Bayesian Trees (REBT)'' . |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Empirical Bayes Estimation / Decision Tree / Random Forest / Generalized Liner Model / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 117, no. 293, IBISML2017-39, pp. 31-38, Nov. 2017. | |
| Paper # | IBISML2017-39 | |
| 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 | IBISML2017-39 |
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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) | Empirical Bayesian Tree |
| Sub Title (in English) | |
| Keyword(1) | Empirical Bayes Estimation |
| Keyword(2) | Decision Tree |
| Keyword(3) | Random Forest |
| Keyword(4) | Generalized Liner Model |
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| 1st Author's Name | Masashi Sekino |
| 1st Author's Affiliation | So-net Media Networks (SMN) |
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| Speaker | Author-1 |
| Date Time | 2017-11-09 13:00:00 |
| Presentation Time | 150 minutes |
| Registration for | IBISML |
| Paper # | IBISML2017-39 |
| Volume (vol) | vol.117 |
| Number (no) | no.293 |
| Page | pp.31-38 |
| #Pages | 8 |
| Date of Issue | 2017-11-02 (IBISML) |
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