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| Presentation | 2020-06-05 11:10 An Analysis on Performance of Laser Chaos Decision Maker by the Method of Surrogate Data Norihiro Okada (TUS), Makoto Naruse, Nicolas Chauvet (UTokyo), Aohan Li, Mikio Hasegawa (TUS)  NLP2020-12 CCS2020-2 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | It has been shown that the decision method based on the laser chaos time series enables ultra-high speed and high performance decision making on the GHz order. In this paper, we apply this method to the Multi-Armed Bandit problem, analyze what features of laser chaos are effective, and try to improve the performance further. First, we compare the performance with respect to the autocorrelation coefficient and show that negative autocorrelation improves the performance. Next, the surrogate data method is introduced to analyze the autocorrelation peculiar to the effective laser chaos time series. As a result of performance comparison between laser chaos time series and three surrogate data, it is shown that the negative correlation peculiar to laser chaos improves the performance, and further improves by making the histogram closer to the Gaussian distribution. Furthermore, as a study of the performance improvement of this method, we show that the performance is improved as a result of adjusting the distribution of the laser chaos time series. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Multi-Armed Bandit Problem / Tug-of-War dynamics / Laser Chaos / The Method of Surrogate Data / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 56, CCS2020-2, pp. 7-12, June 2020. | |
| Paper # | CCS2020-2 | |
| Date of Issue | 2020-05-29 (NLP, CCS) | |
| ISSN | 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 | NLP2020-12 CCS2020-2 |
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| Conference Information | |
| Committee | CCS NLP |
| Conference Date | 2020-06-05 - 2020-06-05 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | CCS |
| Conference Code | 2020-06-CCS-NLP |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | An Analysis on Performance of Laser Chaos Decision Maker by the Method of Surrogate Data |
| Sub Title (in English) | |
| Keyword(1) | Multi-Armed Bandit Problem |
| Keyword(2) | Tug-of-War dynamics |
| Keyword(3) | Laser Chaos |
| Keyword(4) | The Method of Surrogate Data |
| Keyword(5) | |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Norihiro Okada |
| 1st Author's Affiliation | Tokyo University of Science (TUS) |
| 2nd Author's Name | Makoto Naruse |
| 2nd Author's Affiliation | University of Tokyo (UTokyo) |
| 3rd Author's Name | Nicolas Chauvet |
| 3rd Author's Affiliation | University of Tokyo (UTokyo) |
| 4th Author's Name | Aohan Li |
| 4th Author's Affiliation | Tokyo University of Science (TUS) |
| 5th Author's Name | Mikio Hasegawa |
| 5th Author's Affiliation | Tokyo University of Science (TUS) |
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| Speaker | Author-1 |
| Date Time | 2020-06-05 11:10:00 |
| Presentation Time | 25 minutes |
| Registration for | CCS |
| Paper # | NLP2020-12, CCS2020-2 |
| Volume (vol) | vol.120 |
| Number (no) | no.55(NLP), no.56(CCS) |
| Page | pp.7-12 |
| #Pages | 6 |
| Date of Issue | 2020-05-29 (NLP, CCS) |
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