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Paper Abstract and Keywords
Presentation 2010-05-14 14:15
A novel method for minimum entropy clustering using competitive learning
Toshio Uchiyama, Takeharu Eda, Katsuji Bessho, Tadasu Uchiyama, Katsuyoshi Tanabe (NTT) IE2010-39 PRMU2010-27 MI2010-27
Abstract (in Japanese) (See Japanese page) 
(in English) We propose a novel method (algorithm), which uses competitive
learning, for clustering based on
minimizing average entropy in clusters (Minimum entropy
criterion). In each iteration, competitive learning usually
decides the winner of models for an input data and then
updates the model of the winner.
When deciding the winner, minimum entropy criterion requires
calculation of the cross-entropy against the models.
However, it is not easy to manage values of stochastic variables in
models with avoiding zero frequency problem, because competitive
learning updates models so many times. Therefore, we present the idea,
which updates models before deciding a winner, to avoid the zero
frequency problem without any bad influence. We also propose a new
method to apply the clustering technique for improving text
classification accuracy. The effectiveness of our methods is shown by
experiments.
Keyword (in Japanese) (See Japanese page) 
(in English) Clustering / KL-divergence / Minimum entropy criterion / Competitive learning / Naive Bayes classifier / / /  
Reference Info. IEICE Tech. Rep., vol. 110, no. 27, PRMU2010-27, pp. 139-144, May 2010.
Paper # PRMU2010-27 
Date of Issue 2010-05-06 (IE, PRMU, MI) 
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 IE2010-39 PRMU2010-27 MI2010-27

Conference Information
Committee PRMU IE MI  
Conference Date 2010-05-13 - 2010-05-14 
Place (in Japanese) (See Japanese page) 
Place (in English) Chubu Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2010-05-PRMU-IE-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A novel method for minimum entropy clustering using competitive learning 
Sub Title (in English)  
Keyword(1) Clustering  
Keyword(2) KL-divergence  
Keyword(3) Minimum entropy criterion  
Keyword(4) Competitive learning  
Keyword(5) Naive Bayes classifier  
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Keyword(7)  
Keyword(8)  
1st Author's Name Toshio Uchiyama  
1st Author's Affiliation NTT CORPORATION (NTT)
2nd Author's Name Takeharu Eda  
2nd Author's Affiliation NTT CORPORATION (NTT)
3rd Author's Name Katsuji Bessho  
3rd Author's Affiliation NTT CORPORATION (NTT)
4th Author's Name Tadasu Uchiyama  
4th Author's Affiliation NTT CORPORATION (NTT)
5th Author's Name Katsuyoshi Tanabe  
5th Author's Affiliation NTT CORPORATION (NTT)
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Speaker Author-1 
Date Time 2010-05-14 14:15:00 
Presentation Time 30 minutes 
Registration for PRMU 
Paper # IE2010-39, PRMU2010-27, MI2010-27 
Volume (vol) vol.110 
Number (no) no.26(IE), no.27(PRMU), no.28(MI) 
Page pp.139-144 
#Pages
Date of Issue 2010-05-06 (IE, PRMU, MI) 


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