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Paper Abstract and Keywords
Presentation 2019-02-28 13:30
Selection of Gaussian Mixture Reduction Methods Using Machine Learning
Haruki Kazama, Shuji Tsukiyama (Chuo Univ.) VLD2018-113 HWS2018-76
Abstract (in Japanese) (See Japanese page) 
(in English) Gaussian mixture model is a useful distribution for statistical methods such as statistical static timing analysis, but the number of components of Gaussian mixture model increases exponentially by statistical operations. Hence, the number of components must be reduced to around 2 in order to repeat operations effectively and efficiently. Although several methods for reducing the number of components have been proposed, each of them has strength and weakness in accuracy and time complexity. Therefore, selecting an appropriate reduction method for an input distribution is a practical way for reducing the number of components. This paper proposes a selection method using machine learning and evaluates its performance.
Keyword (in Japanese) (See Japanese page) 
(in English) Gaussian mixture model / Gaussian reduction / method selection / support vector machine / experimental results / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 457, VLD2018-113, pp. 121-126, Feb. 2019.
Paper # VLD2018-113 
Date of Issue 2019-02-20 (VLD, HWS) 
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)
Download PDF VLD2018-113 HWS2018-76

Conference Information
Committee HWS VLD  
Conference Date 2019-02-27 - 2019-03-02 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Ken Seinen Kaikan 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Design Technology for System-on-Silicon, Hardware Security, etc. 
Paper Information
Registration To VLD 
Conference Code 2019-02-HWS-VLD 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Selection of Gaussian Mixture Reduction Methods Using Machine Learning 
Sub Title (in English)  
Keyword(1) Gaussian mixture model  
Keyword(2) Gaussian reduction  
Keyword(3) method selection  
Keyword(4) support vector machine  
Keyword(5) experimental results  
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1st Author's Name Haruki Kazama  
1st Author's Affiliation Chuo University (Chuo Univ.)
2nd Author's Name Shuji Tsukiyama  
2nd Author's Affiliation Chuo University (Chuo Univ.)
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Speaker Author-1 
Date Time 2019-02-28 13:30:00 
Presentation Time 25 minutes 
Registration for VLD 
Paper # VLD2018-113, HWS2018-76 
Volume (vol) vol.118 
Number (no) no.457(VLD), no.458(HWS) 
Page pp.121-126 
#Pages
Date of Issue 2019-02-20 (VLD, HWS) 


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