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Paper Abstract and Keywords
Presentation 2010-09-05 09:00
Behavior of kernel mutual subspace method with respect to parameters
Seiji Hotta (TUAT), Tomokazu Kawahara, Osamu Yamaguchi (Toshiba Corp.), Hitoshi Sakano (NTT) PRMU2010-57 IBISML2010-29
Abstract (in Japanese) (See Japanese page) 
(in English) Optimizing parameters of kernel methods is an unsolved problem. We report the experimental evaluation and the consideration of parameters dependencies on kernel mutual subspace method (KMS). The following parameters of KMS are considered:
Gaussian kernel parameters, dimensionalities of dictionary and input subspaces, and the number of canonical angles. We evaluate recognition accuracies of KMS through experiments on ETH-80 animals database. According to searching optimal parameters exhaustively, we obtain 100% recognition accuracy, and some experimental results suggests the relationships between the dimensionality of subspaces and the degrees of freedom for object motions. Such results imply that KMS achieves high recognition rate for object recognition with optimized parameters.
Keyword (in Japanese) (See Japanese page) 
(in English) kernel mutual subspace method / object recognition / subspace method / kernel method / statistical pattern recognition / machine leaning / /  
Reference Info. IEICE Tech. Rep., vol. 110, no. 187, PRMU2010-57, pp. 1-6, Sept. 2010.
Paper # PRMU2010-57 
Date of Issue 2010-08-29 (PRMU, IBISML) 
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 PRMU2010-57 IBISML2010-29

Conference Information
Committee IBISML PRMU IPSJ-CVIM  
Conference Date 2010-09-05 - 2010-09-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Fukuoka Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine learning and optimization for computer vision and pattern recognition, etc. 
Paper Information
Registration To PRMU 
Conference Code 2010-09-IBISML-PRMU-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Behavior of kernel mutual subspace method with respect to parameters 
Sub Title (in English)  
Keyword(1) kernel mutual subspace method  
Keyword(2) object recognition  
Keyword(3) subspace method  
Keyword(4) kernel method  
Keyword(5) statistical pattern recognition  
Keyword(6) machine leaning  
Keyword(7)  
Keyword(8)  
1st Author's Name Seiji Hotta  
1st Author's Affiliation Tokyo University of Agricultural and Technology (TUAT)
2nd Author's Name Tomokazu Kawahara  
2nd Author's Affiliation Toshiba Corporation (Toshiba Corp.)
3rd Author's Name Osamu Yamaguchi  
3rd Author's Affiliation Toshiba Corporation (Toshiba Corp.)
4th Author's Name Hitoshi Sakano  
4th Author's Affiliation NTT Communication Science Lab (NTT)
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Speaker Author-1 
Date Time 2010-09-05 09:00:00 
Presentation Time 30 minutes 
Registration for PRMU 
Paper # PRMU2010-57, IBISML2010-29 
Volume (vol) vol.110 
Number (no) no.187(PRMU), no.188(IBISML) 
Page pp.1-6 
#Pages 6 
Date of Issue 2010-08-29 (PRMU, IBISML) 

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