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Paper Abstract and Keywords
Presentation 2021-03-05 14:45
Analysis of Optimal Error Exponents on Classification for Markov Sources
Hiroto Kuramata, Hideki Yagi, Tsutomu Kawabata (UEC) IT2020-152 ISEC2020-82 WBS2020-71
Abstract (in Japanese) (See Japanese page) 
(in English) We consider a classification problem for a test sequence to determine from which source the sequence generates. The system classifies the test sequence based on empirically observed (training) sequences obtained from unknown sources P1 and P2. Conventionally, the Neyman-Pearson test approach has been used to characterize the asymptotically largest error exponent for stationary and memoryless sources. However, such a characterization has not been known yet for Markov sources. In this paper, we show that the classifier which attains the asymptotically largest error exponent in the class of deterministic classifiers is also optimal in the class of stochastic classifiers for stationary Markov sources of finite order. We then provide a characterization of the error exponents using Markov types. As a result, a new characterization of the error exponent is also obtained for stationary memoryless sources.
Keyword (in Japanese) (See Japanese page) 
(in English) Classification problem / Hypothesis test / Error exponent / Markov sources / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 410, IT2020-152, pp. 245-250, March 2021.
Paper # IT2020-152 
Date of Issue 2021-02-25 (IT, ISEC, WBS) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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 IT2020-152 ISEC2020-82 WBS2020-71

Conference Information
Committee WBS IT ISEC  
Conference Date 2021-03-04 - 2021-03-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Joint Meeting of WBS, IT, and ISEC 
Paper Information
Registration To IT 
Conference Code 2021-03-WBS-IT-ISEC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Analysis of Optimal Error Exponents on Classification for Markov Sources 
Sub Title (in English)  
Keyword(1) Classification problem  
Keyword(2) Hypothesis test  
Keyword(3) Error exponent  
Keyword(4) Markov sources  
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1st Author's Name Hiroto Kuramata  
1st Author's Affiliation University of Electro-Communications (UEC)
2nd Author's Name Hideki Yagi  
2nd Author's Affiliation University of Electro-Communications (UEC)
3rd Author's Name Tsutomu Kawabata  
3rd Author's Affiliation University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2021-03-05 14:45:00 
Presentation Time 25 minutes 
Registration for IT 
Paper # IT2020-152, ISEC2020-82, WBS2020-71 
Volume (vol) vol.120 
Number (no) no.410(IT), no.411(ISEC), no.412(WBS) 
Page pp.245-250 
#Pages
Date of Issue 2021-02-25 (IT, ISEC, WBS) 


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