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Paper Abstract and Keywords
Presentation 2016-11-17 14:00
An Exhaustive Search with Support Vector Machine (ES-SVM) for sparse variable selection
Daiki Kawabata (UTokyo), Hiroko Ichikawa (TUS), Yasuhiko Igarashi (UTokyo), Kenji Nagata (AIST/JST/UTokyo), Satoshi Eifuku, Ryoi Tamura (Toyama Univ.), Masato Okada (UTokyo) IBISML2016-96
Abstract (in Japanese) (See Japanese page) 
(in English) Nagata et al.(2015) has proposed Exhaustive Search with Support Vector Machine(ES-SVM) which calculates a cross validation error(CVE) by Support Vector Machine for all combinations of variables in order to make the histogram of CVE.In this study, we propose a novel method for sparse variable selection called ESn-SVM for classification.The method assumes sparsity to the true solution and increases the variables which useful for classification from one by one.In order to efficiently perform ESn-SVM, we propose an systematic approach by the replica exchange Monte Carlo (REMC) method and the multiple histogram method.
Keyword (in Japanese) (See Japanese page) 
(in English) variable selection / liner classification / exhaustive search / support vector machine (SVM) / density of states / replica exchange Monte Carlo (REMC) / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 300, IBISML2016-96, pp. 361-368, Nov. 2016.
Paper # IBISML2016-96 
Date of Issue 2016-11-09 (IBISML) 
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 IBISML  
Conference Date 2016-11-16 - 2016-11-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyoto Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information-Based Induction Science Workshop (IBIS2016) 
Paper Information
Registration To IBISML 
Conference Code 2016-11-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An Exhaustive Search with Support Vector Machine (ES-SVM) for sparse variable selection 
Sub Title (in English)  
Keyword(1) variable selection  
Keyword(2) liner classification  
Keyword(3) exhaustive search  
Keyword(4) support vector machine (SVM)  
Keyword(5) density of states  
Keyword(6) replica exchange Monte Carlo (REMC)  
Keyword(7)  
Keyword(8)  
1st Author's Name Daiki Kawabata  
1st Author's Affiliation Univercity of Tokyo (UTokyo)
2nd Author's Name Hiroko Ichikawa  
2nd Author's Affiliation Tokyo University of Science (TUS)
3rd Author's Name Yasuhiko Igarashi  
3rd Author's Affiliation Univercity of Tokyo (UTokyo)
4th Author's Name Kenji Nagata  
4th Author's Affiliation National Institute of Advanced Industrial Science and Technology/Japan Science and Technology Agency /Univercity of Tokyo (AIST/JST/UTokyo)
5th Author's Name Satoshi Eifuku  
5th Author's Affiliation Univercity of Toyama (Toyama Univ.)
6th Author's Name Ryoi Tamura  
6th Author's Affiliation Univercity of Toyama (Toyama Univ.)
7th Author's Name Masato Okada  
7th Author's Affiliation Univercity of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2016-11-17 14:00:00 
Presentation Time 180 minutes 
Registration for IBISML 
Paper # IBISML2016-96 
Volume (vol) vol.116 
Number (no) no.300 
Page pp.361-368 
#Pages 8 
Date of Issue 2016-11-09 (IBISML) 

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