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Paper Abstract and Keywords
Presentation 2016-11-17 14:00
Budgeted stream-based active learning via adaptive submodular maximization
Kaito Fujii, Hisashi Kashima (Kyoto Univ.) IBISML2016-74
Abstract (in Japanese) (See Japanese page) 
(in English) Active learning enables us to reduce the annotation cost by adaptively selecting unlabeled instances to be labeled. For pool-based active learning, several effective methods with theoretical guarantees have been developed through maximizing some utility function satisfying adaptive submodularity. In contrast, there have been few methods for stream-based active learning based on adaptive submodularity. In this paper, we propose a new class of utility functions, policy-adaptive submodular functions, which includes many existing adaptive submodular functions appearing in real world problems. We provide a general framework based on policy-adaptive submodularity that makes it possible to convert existing pool-based methods to stream-based methods and give theoretical guarantees on their performance. In addition we empirically demonstrate their effectiveness by comparing with existing heuristics on common benchmark datasets.
Keyword (in Japanese) (See Japanese page) 
(in English) stream-based active learning / adaptive submodular maximization / streaming algorithms / secretary problem / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 300, IBISML2016-74, pp. 199-206, Nov. 2016.
Paper # IBISML2016-74 
Date of Issue 2016-11-09 (IBISML) 
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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Conference Information
Committee IBISML  
Conference Date 2016-11-16 - 2016-11-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyoto Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information-Based Induction Science Workshop (IBIS2016) 
Paper Information
Registration To IBISML 
Conference Code 2016-11-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Budgeted stream-based active learning via adaptive submodular maximization 
Sub Title (in English)  
Keyword(1) stream-based active learning  
Keyword(2) adaptive submodular maximization  
Keyword(3) streaming algorithms  
Keyword(4) secretary problem  
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1st Author's Name Kaito Fujii  
1st Author's Affiliation Kyoto University (Kyoto Univ.)
2nd Author's Name Hisashi Kashima  
2nd Author's Affiliation Kyoto University (Kyoto Univ.)
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Speaker Author-1 
Date Time 2016-11-17 14:00:00 
Presentation Time 180 minutes 
Registration for IBISML 
Paper # IBISML2016-74 
Volume (vol) vol.116 
Number (no) no.300 
Page pp.199-206 
#Pages 8 
Date of Issue 2016-11-09 (IBISML) 

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