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Paper Abstract and Keywords
Presentation 2017-02-18 11:45
Formal concept analysis based deep learning for image content abstraction
Hayato Shimodaira, Hajime Nobuhara (Univ. of Tsukuba) PRMU2016-160 CNR2016-27
Abstract (in Japanese) (See Japanese page) 
(in English) The proposed method consists of a concept lattice construction process and an information recommendation process. The Formal Concept Analysis is used in the concept lattice construction process. In order to show the effectiveness of the proposed method, we demonstrate the possibility of abstraction representation and its effectiveness on real human abstraction level understanding.
Keyword (in Japanese) (See Japanese page) 
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Reference Info. IEICE Tech. Rep., vol. 116, no. 461, PRMU2016-160, pp. 47-52, Feb. 2017.
Paper # PRMU2016-160 
Date of Issue 2017-02-11 (PRMU, CNR) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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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)
Download PDF PRMU2016-160 CNR2016-27

Conference Information
Committee PRMU CNR  
Conference Date 2017-02-18 - 2017-02-19 
Place (in Japanese) (See Japanese page) 
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Paper Information
Registration To PRMU 
Conference Code 2017-02-PRMU-CNR 
Language Japanese 
Title (in Japanese) (See Japanese page) 
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Title (in English) Formal concept analysis based deep learning for image content abstraction 
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1st Author's Name Hayato Shimodaira  
1st Author's Affiliation University of Tsukuba (Univ. of Tsukuba)
2nd Author's Name Hajime Nobuhara  
2nd Author's Affiliation University of Tsukuba (Univ. of Tsukuba)
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Date Time 2017-02-18 11:45:00 
Presentation Time 25 minutes 
Registration for PRMU 
Paper # PRMU2016-160, CNR2016-27 
Volume (vol) vol.116 
Number (no) no.461(PRMU), no.462(CNR) 
Page pp.47-52 
#Pages
Date of Issue 2017-02-11 (PRMU, CNR) 


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