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Paper Abstract and Keywords
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
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
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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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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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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
Date Time 2017-03-20 10:00:00 
Presentation Time 25 
Registration for PRMU 
Paper # IEICE-BioX2016-33,IEICE-PRMU2016-196 
Volume (vol) IEICE-116 
Number (no) no.527(BioX), no.528(PRMU) 
Page pp.1-6 
#Pages IEICE-6 
Date of Issue IEICE-BioX-2017-03-13,IEICE-PRMU-2017-03-13 


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