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Paper Abstract and Keywords
Presentation 2015-01-23 10:15
Relation between Data Grouping and Robustness to Unseen Data in Large Geometric Margin Minimum Classification Error Training
Hiroyuki Shiraishi (Doshisha Univ), Hideyuki Watanabe (NICT), Shigeru Katagiri (Doshisha Univ), Xugang Lu, Chiori Hori (NICT), Miho Ohsaki (Doshisha Univ) PRMU2014-101 MVE2014-63
Abstract (in Japanese) (See Japanese page) 
(in English) To develop a pattern classifier that is robust to unseen pattern samples, classifier parameters have been conventionally trained using both training and validation sample sets. However, there are no clear criteria for dividing the samples in hand into training and validation sets. In addition, such grouping decreases the number of samples for both training and validation, often lowering robustness to unseen samples. To solve this problem, we elaborate in this paper the nature of an approach that aims, without validation samples, for high robustness only with Large Geometric Margin Minimum Classification Error training over training samples. From experiments using several different sizes of training/validation sample sets, we clarify the advantages and disadvantages of the conventional approach using validation samples and show the potential utility of our proposed large-geometric-margin-based approach.
Keyword (in Japanese) (See Japanese page) 
(in English) Pattern recognition / Minimum classification error training / Geometric margin / Data grouping for training / / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 409, PRMU2014-101, pp. 177-182, Jan. 2015.
Paper # PRMU2014-101 
Date of Issue 2015-01-15 (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 2015-01-22 - 2015-01-23 
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 2015-01-PRMU-CVIM-MVE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Relation between Data Grouping and Robustness to Unseen Data in Large Geometric Margin Minimum Classification Error Training 
Sub Title (in English)  
Keyword(1) Pattern recognition  
Keyword(2) Minimum classification error training  
Keyword(3) Geometric margin  
Keyword(4) Data grouping for training  
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1st Author's Name Hiroyuki Shiraishi  
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 Xugang Lu  
4th Author's Affiliation National Institute of Information and Communications Technology (NICT)
5th Author's Name Chiori Hori  
5th Author's Affiliation National Institute of Information and Communications Technology (NICT)
6th Author's Name Miho Ohsaki  
6th Author's Affiliation Doshisha University (Doshisha Univ)
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Speaker Author-1 
Date Time 2015-01-23 10:15:00 
Presentation Time 25 minutes 
Registration for PRMU 
Paper # PRMU2014-101, MVE2014-63 
Volume (vol) vol.114 
Number (no) no.409(PRMU), no.410(MVE) 
Page pp.177-182 
#Pages 6 
Date of Issue 2015-01-15 (PRMU, MVE) 

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