| Presentation | 2017-06-24 Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags Han Bao, Tomoya Sakai, Issei Sato, Masashi Sugiyama, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
| Keyword(in English) | Multiple Instance Learning / PU learning |
| Paper # | IBISML2017-3 |
| Date of Issue | 2017-06-17 (IBISML) |
| Conference Information | |
| Committee | NC / IPSJ-BIO / IBISML / IPSJ-MPS |
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| Conference Date | 2017/6/23(3days) |
| 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 |
| Chair | Masafumi Hagiwara(Keio Univ.) / / Kenji Fukumizu(ISM) |
| Vice Chair | Yutaka Hirata(Chubu Univ.) / / Masashi Sugiyama(Univ. of Tokyo) |
| Secretary | Yutaka Hirata(Tokyo Inst. of Tech.) / (Nagoya Univ.) / Masashi Sugiyama / (Kyoto Univ.) |
| Assistant | Yoshihisa Shinozawa(Keio Univ.) / Keiichiro Inagaki(Chubu Univ.) / / Ichiro Takeuchi(Nagoya Inst. of Tech.) / Toshihiro Kamishima(AIST) |
| Paper Information | |
| Registration To | Technical Committee on Neurocomputing / Special Interest Group on Bioinformatics and Genomics / Technical Committee on Infomation-Based Induction Sciences and Machine Learning / Special Interest Group on Mathematical Modeling and Problem Solving |
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| Language | ENG |
| 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) |
| Date | 2017-06-24 |
| Paper # | IBISML2017-3 |
| Volume (vol) | vol.117 |
| Number (no) | IBISML-110 |
| Page | pp.pp.55-62(IBISML), |
| #Pages | 8 |
| Date of Issue | 2017-06-17 (IBISML) |