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Paper Abstract and Keywords
Presentation 2017-11-10 13:00
[Poster Presentation] Multi-Task Learning with Positive and Unlabeled Data and Its Application to Mental State Prediction
Hirotaka Kaji, Hayato Yamaguchi (Toyota Motor), Masashi Sugiyama (RIKEN/UTokyo) IBISML2017-66
Abstract (in Japanese) (See Japanese page) 
(in English) In real-world machine learning applications, we are often faced with a situation where only a small number of training samples is available due to high sampling costs. For instance, prediction of mental states such
as drowsiness from physiological information is a typical example. To cope with this problem, classifier training methods only from positive and unlabeled data and multi-task learning methods for improving the classification performance by solving multiple related tasks simultaneously have been actively investigated recently. In this paper,
we combine these methods and propose a multi-task learning method that can handle positive-unlabeled tasks and positive-negative tasks in a unified manner. Through experiments on drivers’ drowsiness prediction, we demonstrate the effectiveness of the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) Multi-task learning / positive and unlabeled learning / mental state prediction / / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 293, IBISML2017-66, pp. 235-242, Nov. 2017.
Paper # IBISML2017-66 
Date of Issue 2017-11-02 (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)
Download PDF IBISML2017-66

Conference Information
Committee IBISML  
Conference Date 2017-11-08 - 2017-11-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Univ. of Tokyo 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information-Based Induction Science Workshop (IBIS2017) 
Paper Information
Registration To IBISML 
Conference Code 2017-11-IBISML 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Multi-Task Learning with Positive and Unlabeled Data and Its Application to Mental State Prediction 
Sub Title (in English)  
Keyword(1) Multi-task learning  
Keyword(2) positive and unlabeled learning  
Keyword(3) mental state prediction  
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1st Author's Name Hirotaka Kaji  
1st Author's Affiliation Toyota Motor Corp. (Toyota Motor)
2nd Author's Name Hayato Yamaguchi  
2nd Author's Affiliation Toyota Motor Corp. (Toyota Motor)
3rd Author's Name Masashi Sugiyama  
3rd Author's Affiliation RIKEN/The University of Tokyo (RIKEN/UTokyo)
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Speaker Author-1 
Date Time 2017-11-10 13:00:00 
Presentation Time 150 minutes 
Registration for IBISML 
Paper # IBISML2017-66 
Volume (vol) vol.117 
Number (no) no.293 
Page pp.235-242 
#Pages 8 
Date of Issue 2017-11-02 (IBISML) 

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