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Learning From Positive and Unlabeled Data: A Survey
[article]
2018
arXiv
pre-print
Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in
arXiv:1811.04820v1
fatcat:2qi5g4xke5fljpw2iz7nhudc5a