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| Presentation | 2018-03-06 13:35 Applicability of Fast Decimation Algorithm -- Sparse two-parameter Boltzmann machine as a benchmark function -- Daisuke Motoki, Shohei Watabe, Tetsuro Nikuni (Tokyo Univ. of Science)  IBISML2017-103 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | A decimation algorithm was developed by Decelle et al. for an inverse problem optimization method, which sequentially reduces the number of parameters for each epoch of machine learning.This algorithm has the merit that arbitrariness of parameters of L1 regularization method which is often used in sparse modeling can be removed. However, the decimation algorithm has problems in terms of calculation accuracy and speed.In order to improve the problems raised above, we developed Fast Decimation Algorithm that does not use maximum likelihood in the early epochs of learning. We applied our algorithm to a two-parameter Boltzmann Machine applicable to Item Responsive Theory with relevant structure between examinees and demonstrated its usefulness by making use of the Area Under the ROC Curve (AUC). | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Statistical Machine Learning / Sparse Modeling / Boltzmann Machine / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 117, no. 475, IBISML2017-103, pp. 91-95, March 2018. | |
| Paper # | IBISML2017-103 | |
| Date of Issue | 2018-02-26 (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 | IBISML2017-103 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2018-03-05 - 2018-03-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Nishijin Plaza, Kyushu University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Statisitical Mathematics, Machine Learning, Data Mining, etc. |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2018-03-IBISML |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Applicability of Fast Decimation Algorithm |
| Sub Title (in English) | Sparse two-parameter Boltzmann machine as a benchmark function |
| Keyword(1) | Statistical Machine Learning |
| Keyword(2) | Sparse Modeling |
| Keyword(3) | Boltzmann Machine |
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| 1st Author's Name | Daisuke Motoki |
| 1st Author's Affiliation | Tokyo University of Science (Tokyo Univ. of Science) |
| 2nd Author's Name | Shohei Watabe |
| 2nd Author's Affiliation | Tokyo University of Science (Tokyo Univ. of Science) |
| 3rd Author's Name | Tetsuro Nikuni |
| 3rd Author's Affiliation | Tokyo University of Science (Tokyo Univ. of Science) |
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| Speaker | Author-1 |
| Date Time | 2018-03-06 13:35:00 |
| Presentation Time | 25 minutes |
| Registration for | IBISML |
| Paper # | IBISML2017-103 |
| Volume (vol) | vol.117 |
| Number (no) | no.475 |
| Page | pp.91-95 |
| #Pages | 5 |
| Date of Issue | 2018-02-26 (IBISML) |
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