| IEICE Technical Committee Submission System Conference Paper's Information |
Online Proceedings [Sign in] Tech. Rep. Archives |
| Go Top Page | Go Previous | [Japanese] / [English] |
| 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 | |
| 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 | IBISML2017-3 |
|
| Conference Information | |
| Committee | NC IPSJ-BIO IBISML IPSJ-MPS |
| 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 |
| Keyword(3) | |
| Keyword(4) | |
| Keyword(5) | |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 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) |
| 5th Author's Name | |
| 5th Author's Affiliation | () |
| 6th Author's Name | |
| 6th Author's Affiliation | () |
| 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 |
| #Pages | 8 |
| Date of Issue | 2017-06-17 (IBISML) |
[Return to Top Page]