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 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) |
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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) |
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Gaussian mixture model |
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Gaussian reduction |
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method selection |
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support vector machine |
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experimental results |
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1st Author's Name |
Haruki Kazama |
1st Author's Affiliation |
Chuo University (Chuo Univ.) |
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Shuji Tsukiyama |
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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 |
6 |
Date of Issue |
2019-02-20 (VLD, HWS) |
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