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| Presentation | 2017-11-10 13:00 [Poster Presentation] Binary Classification from Positive-Confidence Data Takashi Ishida (SMAM/UTokyo/RIKEN), Gang Niu (UTokyo/RIKEN), Masashi Sugiyama (RIKEN/UTokyo)  IBISML2017-62 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Reducing labeling costs in supervised learning is a critical issue in many practical machine learning applications. In this paper, we consider positive-confidence (Pconf) classification, the problem of training a binary classifier only from positive data equipped with confidence. Pconf classification can be regarded as a discriminative extension of one-class classification (which is aimed at ``describing'' the positive class), with ability to tune hyper-parameters for ``classifying'' positive and negative samples. Pconf classification is also related to positive-unlabeled (PU) classification (which uses hard-labeled positive data and unlabeled data), allowing us to avoid estimating the class priors, which is a critical bottleneck in typical PU classification methods. For the Pconf classification problem, we provide a simple empirical risk minimization framework and give a formulation for linear-in-parameter models that can be implemented easily and computationally efficiently. We also theoretically establish the consistency and generalization error bounds for Pconf classification, and demonstrate the practical usefulness of the proposed method through experiments. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | pconf / positive-confidence / weakly-supervised learning / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 117, no. 293, IBISML2017-62, pp. 207-214, Nov. 2017. | |
| Paper # | IBISML2017-62 | |
| 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 | IBISML2017-62 |
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| 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) | Binary Classification from Positive-Confidence Data |
| Sub Title (in English) | |
| Keyword(1) | pconf |
| Keyword(2) | positive-confidence |
| Keyword(3) | weakly-supervised learning |
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| 1st Author's Name | Takashi Ishida |
| 1st Author's Affiliation | Sumitomo Mitsui Asset Management/The University of Tokyo/RIKEN (SMAM/UTokyo/RIKEN) |
| 2nd Author's Name | Gang Niu |
| 2nd Author's Affiliation | The University of Tokyo/RIKEN (UTokyo/RIKEN) |
| 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 # | IBISML2017-62 |
| Volume (vol) | vol.117 |
| Number (no) | no.293 |
| Page | pp.207-214 |
| #Pages | 8 |
| Date of Issue | 2017-11-02 (IBISML) |
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