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Paper Abstract and Keywords
Presentation 2014-01-23 09:30
Minimum Classification Error Training with Automatic Determination of Loss Smoothness Common to All Classes
Kensuke Ota (Doshisha Univ.), Hideyuki Watanabe (NICT), Shigeru Katagiri, Miho Ohsaki (Doshisha Univ.), Shigeki Matsuda, Chiori Hori (NICT) PRMU2013-91 MVE2013-32
Abstract (in Japanese) (See Japanese page) 
(in English) The smoothness of the smooth classification error count loss used in the Minimum Classification Error (MCE) training has an effect of increasing training robustness to unseen samples. Therefore, an appropriate determination of the smoothness is obviously needed. Recently, to meet this necessity, a method using the Parzen-estimation-based MCE formalization was proposed for automatically setting the smoothness, and its effectiveness was demonstrated. However, this method sets the smoothness in the class-by-class mode, and it has a potential risk of causing over-fitting to training samples. In this paper, we propose a new method for automatically finding an appropriate value of the smoothness that is set to all of the classes, and demonstrate its usefulness. From evaluation experiments, we show that the proposed method works more stably and more effectively under various classifier conditions than its counterpart, preceding automatic determination method.
Keyword (in Japanese) (See Japanese page) 
(in English) Minimum classification error training / Parzen estimation / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 113, no. 402, PRMU2013-91, pp. 1-6, Jan. 2014.
Paper # PRMU2013-91 
Date of Issue 2014-01-16 (PRMU, MVE) 
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 IPSJ-CVIM MVE  
Conference Date 2014-01-23 - 2014-01-24 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To PRMU 
Conference Code 2014-01-PRMU-CVIM-MVE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Minimum Classification Error Training with Automatic Determination of Loss Smoothness Common to All Classes 
Sub Title (in English)  
Keyword(1) Minimum classification error training  
Keyword(2) Parzen estimation  
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1st Author's Name Kensuke Ota  
1st Author's Affiliation Doshisha University (Doshisha Univ.)
2nd Author's Name Hideyuki Watanabe  
2nd Author's Affiliation National Institute of Information and Communications Technology (NICT)
3rd Author's Name Shigeru Katagiri  
3rd Author's Affiliation Doshisha University (Doshisha Univ.)
4th Author's Name Miho Ohsaki  
4th Author's Affiliation Doshisha University (Doshisha Univ.)
5th Author's Name Shigeki Matsuda  
5th Author's Affiliation National Institute of Information and Communications Technology (NICT)
6th Author's Name Chiori Hori  
6th Author's Affiliation National Institute of Information and Communications Technology (NICT)
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Speaker Author-1 
Date Time 2014-01-23 09:30:00 
Presentation Time 30 minutes 
Registration for PRMU 
Paper # PRMU2013-91, MVE2013-32 
Volume (vol) vol.113 
Number (no) no.402(PRMU), no.403(MVE) 
Page pp.1-6 
#Pages 6 
Date of Issue 2014-01-16 (PRMU, MVE) 

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