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- W2123958887 abstract "The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and the other set consists of negative examples. However, it is often the case that the available training data are an incomplete set of positive examples, and a set of unlabeled examples, some of which are positive and some of which are negative. The problem solved in this paper is how to learn a standard binary classifier given a nontraditional training set of this nature." @default.
- W2123958887 created "2016-06-24" @default.
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- W2123958887 date "2008-08-24" @default.
- W2123958887 modified "2023-10-18" @default.
- W2123958887 title "Learning classifiers from only positive and unlabeled data" @default.
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- W2123958887 doi "https://doi.org/10.1145/1401890.1401920" @default.
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