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Can we learn a binary classifier from only positive data, without any negative data or unlabeled data? We show that if one can equip positive data with confidence (positive-confidence), one can successfully learn a binary classifier, which we name positive-confidence (Pconf) classification. Our work is related to one-class classification which is aimed at "describing" the positive class by clustering-related methods, but one-class classification does not have the ability to tune hyper-parameters and their aim is not on "discriminating" positive and negative classes. For the Pconf classification problem, we provide a simple empirical risk minimization framework that is model-independent and optimization-independent. We theoretically establish the consistency and an estimation error bound, and demonstrate the usefulness of the proposed method for training deep neural networks through experiments.
Author Information
Takashi Ishida (The University of Tokyo, RIKEN, SMAM)
Gang Niu (RIKEN)
Masashi Sugiyama (RIKEN / University of Tokyo)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Spotlight: Binary Classification from Positive-Confidence Data »
Thu. Dec 6th 09:45 -- 09:50 PM Room Room 220 CD
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