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| Presentation | 2017-03-20 10:00 Selection of Near-Boundary Data for Semi-Supervised Learning Ryohei Tanaka, Xiao Ding, Soichiro Ono, Akio Furuhata (Toshiba)  BioX2016-33 PRMU2016-196 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Semi-supervised learning (SSL) is a technique which makes use of unlabeled data in addition to labeled data to obtain better learning accuracies. The most fundamental and widely applicable subtype of SSL is called self-training, which produces additional labeled data from unlabeled data using the results of the classifier trained with the existing labeled data. Conventionally, the additional labeling is performed only on unlabeled data with high prediction confidence above the built-in threshold predetermined by the designer. On the other hand, it is known that learning data near the decision boundary play a crucial role for classification performances. In this paper, we introduce this knowledge to self-training by selectively labeling unconfident unlabeled data near the decision boundary. We also propose a novel method using optimal region accumulation which automatically optimizes the labeling threshold to accumulate data near the boundary. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Semi-supervised learning / self-training / subspace method / handwritten digits recognition / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 116, no. 528, PRMU2016-196, pp. 1-6, March 2017. | |
| Paper # | PRMU2016-196 | |
| Date of Issue | 2017-03-13 (BioX, PRMU) | |
| 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 | BioX2016-33 PRMU2016-196 |
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| Conference Information | |
| Committee | PRMU BioX |
| Conference Date | 2017-03-20 - 2017-03-21 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2017-03-PRMU-BioX |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Selection of Near-Boundary Data for Semi-Supervised Learning |
| Sub Title (in English) | |
| Keyword(1) | Semi-supervised learning |
| Keyword(2) | self-training |
| Keyword(3) | subspace method |
| Keyword(4) | handwritten digits recognition |
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| Keyword(8) | |
| 1st Author's Name | Ryohei Tanaka |
| 1st Author's Affiliation | Toshiba Corporation (Toshiba) |
| 2nd Author's Name | Xiao Ding |
| 2nd Author's Affiliation | Toshiba Corporation (Toshiba) |
| 3rd Author's Name | Soichiro Ono |
| 3rd Author's Affiliation | Toshiba Corporation (Toshiba) |
| 4th Author's Name | Akio Furuhata |
| 4th Author's Affiliation | Toshiba Corporation (Toshiba) |
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| Speaker | Author-1 |
| Date Time | 2017-03-20 10:00:00 |
| Presentation Time | 25 minutes |
| Registration for | PRMU |
| Paper # | BioX2016-33, PRMU2016-196 |
| Volume (vol) | vol.116 |
| Number (no) | no.527(BioX), no.528(PRMU) |
| Page | pp.1-6 |
| #Pages | 6 |
| Date of Issue | 2017-03-13 (BioX, PRMU) |
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