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For many interesting tasks, such as medical diagnosis and web page classification, a learner only has access to some positively labeled examples and many unlabeled examples.
Inference and missing data
D.B. Rubin · 1976
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Pac learning from positive statistical queries
F. Denis · 1998
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Positive and unlabeled examples help learning
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Learning with positive and unlabeled examples using weighted logistic regression
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Learning from positive and unlabeled examples
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Partially supervised classification–based on weighted unlabeled samples support vector machine
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Mixture proportion estimation via kernel embedding of distributions
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Recommendations as treatments: Debiasing learning and evaluation
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F. Mordelet and J-P Vert · 2014
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Estimating the class prior in positive and unlabeled data through decision tree induction
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Estimating the class prior and posterior from noisy positives and unlabeled data
S. Jain, M. White, and P. Radivojac
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Nonparametric semi-supervised learning of class proportions
S. Jain, M. White, M. W. Trosset, and P. Radivojac
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Positive and unlabeled relational classification through label frequency estimation
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Estimating rule quality for knowledge base completion with the relationship between coverage assumption
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