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Paper Abstract and Keywords
Presentation 2017-06-24 10:20
Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags
Han Bao (Univ. of Tokyo), Tomoya Sakai, Issei Sato (Univ. of Tokyo/RIKEN), Masashi Sugiyama (RIKEN/Univ. of Tokyo) IBISML2017-3
Abstract (in Japanese) (See Japanese page) 
(in English) Multiple instance learning (MIL) is a variation of traditional supervised learning problems where data (referred to as bags) are composed of sub-elements (referred to as instances) and only bag labels are available.
MIL has a variety of applications such as content-based image retrieval, text categorization and medical diagnosis.
Most of the previous work for MIL assume that the training bags are fully labeled.
However, it is often difficult to obtain an enough number of labeled bags in practical situations, while many unlabeled bags are available.
A learning framework called PU learning (positive and unlabeled learning) can address this problem.
In this paper, we propose a convex PU learning method to solve an MIL problem.
We experimentally show that the proposed method achieves better performance with significantly lower computational costs than an existing method for PU-MIL.
Keyword (in Japanese) (See Japanese page) 
(in English) Multiple Instance Learning / PU learning / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 110, IBISML2017-3, pp. 55-62, June 2017.
Paper # IBISML2017-3 
Date of Issue 2017-06-17 (IBISML) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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
Conference Date 2017-06-23 - 2017-06-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Institute of Science and Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Machine Learning Approach to Biodata Mining, and General 
Paper Information
Registration To IBISML 
Conference Code 2017-06-NC-BIO-IBISML-MPS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags 
Sub Title (in English)  
Keyword(1) Multiple Instance Learning  
Keyword(2) PU learning  
1st Author's Name Han Bao  
1st Author's Affiliation The University of Tokyo (Univ. of Tokyo)
2nd Author's Name Tomoya Sakai  
2nd Author's Affiliation The University of Tokyo/RIKEN (Univ. of Tokyo/RIKEN)
3rd Author's Name Issei Sato  
3rd Author's Affiliation The University of Tokyo/RIKEN (Univ. of Tokyo/RIKEN)
4th Author's Name Masashi Sugiyama  
4th Author's Affiliation RIKEN/The University of Tokyo (RIKEN/Univ. of Tokyo)
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Speaker Author-1 
Date Time 2017-06-24 10:20:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # IBISML2017-3 
Volume (vol) vol.117 
Number (no) no.110 
Page pp.55-62 
Date of Issue 2017-06-17 (IBISML) 

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