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Paper Abstract and Keywords
Presentation 2015-03-16 15:10
A Proposal of Novel Data Detection Method and Its Application to Incremental Learning for RBMs
Masahiko Osawa, Masafumi Hagiwara (Keio Univ.) MBE2014-167 NC2014-118
Abstract (in Japanese) (See Japanese page) 
(in English) Incremental learnings without destruction of the existing memory are often difficult for deep learning, since most of the weights change. In this paper, we propose an incremental learning method for Restricted Boltzmann Machines (RBMs). First, we suggest that trained RBMs can detect novel data using their energy function. Second, we propose the incremental learning method for these novel data by adding some new units and combine them with the trained network. The proposed method can memorize novel data and without degradating learned contents. According to the evaluation experiments, it is suggested that RBMs can find novel data using the energy function and learn without destruction of learned contents. Furthermore, in the task of the tick-tack-toe, the agents using the proposed method could get strategies progressively and they were better than agents without these methods even if the latter learned much more data.
Keyword (in Japanese) (See Japanese page) 
(in English) Restricted Boltzmann Machine / Deep Learning / Associative Memory / Incremental Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 515, NC2014-118, pp. 283-288, March 2015.
Paper # NC2014-118 
Date of Issue 2015-03-09 (MBE, NC) 
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 MBE2014-167 NC2014-118

Conference Information
Committee NC MBE  
Conference Date 2015-03-16 - 2015-03-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Tamagawa University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) ME, etc 
Paper Information
Registration To NC 
Conference Code 2015-03-NC-MBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Proposal of Novel Data Detection Method and Its Application to Incremental Learning for RBMs 
Sub Title (in English)  
Keyword(1) Restricted Boltzmann Machine  
Keyword(2) Deep Learning  
Keyword(3) Associative Memory  
Keyword(4) Incremental Learning  
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1st Author's Name Masahiko Osawa  
1st Author's Affiliation Keio University (Keio Univ.)
2nd Author's Name Masafumi Hagiwara  
2nd Author's Affiliation Keio University (Keio Univ.)
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Speaker Author-1 
Date Time 2015-03-16 15:10:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # MBE2014-167, NC2014-118 
Volume (vol) vol.114 
Number (no) no.514(MBE), no.515(NC) 
Page pp.283-288 
#Pages
Date of Issue 2015-03-09 (MBE, NC) 


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