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- W2942826900 abstract "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." @default.
- W2942826900 created "2019-05-09" @default.
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- W2942826900 date "2017-11-02" @default.
- W2942826900 modified "2023-10-03" @default.
- W2942826900 title "Binary Classification from Positive-Confidence Data" @default.
- W2942826900 hasPublicationYear "2017" @default.
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