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| Presentation | 2018-09-20 09:40 Arrangement of Complementary Weak Learners using Weights Assigned to Data in Parallel Ensemble Learning Shota Utsumi, Keisuke Kameyama (Univ. of Tsukuba)  PRMU2018-37 IBISML2018-14 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The accuracy of each weak learner and acquisition of complementary functions among weak learners are important for improving the generalization performance in Ensemble Learning. In this paper, we propose a method in which weak learners are trained in parallel based on the weights of the samples used in Boosting. A sample which has large weight is misclassified by many weak learners, thus we aim to improve complementation between weak learners by considering such heavily weighted samples in training weak learners. Through experiments, it was found that the complementation between weak learners and the accuracies of the combined learner of the proposed method are superior to the conventional method. Additionally we discuss complementation in weak learners and accuracies of the proposed method. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Ensemble Learning / Boosting / AdaBoost / Error Correcting Output Codes / Multi-Class Classification / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 220, IBISML2018-14, pp. 9-15, Sept. 2018. | |
| Paper # | IBISML2018-14 | |
| Date of Issue | 2018-09-13 (PRMU, IBISML) | |
| ISSN | 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 | PRMU2018-37 IBISML2018-14 |
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| Conference Information | |
| Committee | PRMU IBISML IPSJ-CVIM |
| Conference Date | 2018-09-20 - 2018-09-21 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2018-09-PRMU-IBISML-CVIM |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Arrangement of Complementary Weak Learners using Weights Assigned to Data in Parallel Ensemble Learning |
| Sub Title (in English) | |
| Keyword(1) | Ensemble Learning |
| Keyword(2) | Boosting |
| Keyword(3) | AdaBoost |
| Keyword(4) | Error Correcting Output Codes |
| Keyword(5) | Multi-Class Classification |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Shota Utsumi |
| 1st Author's Affiliation | University of Tsukuba (Univ. of Tsukuba) |
| 2nd Author's Name | Keisuke Kameyama |
| 2nd Author's Affiliation | University of Tsukuba (Univ. of Tsukuba) |
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| Speaker | Author-1 |
| Date Time | 2018-09-20 09:40:00 |
| Presentation Time | 10 minutes |
| Registration for | IBISML |
| Paper # | PRMU2018-37, IBISML2018-14 |
| Volume (vol) | vol.118 |
| Number (no) | no.219(PRMU), no.220(IBISML) |
| Page | pp.9-15 |
| #Pages | 7 |
| Date of Issue | 2018-09-13 (PRMU, IBISML) |
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