Positive-Unlabeled Learning with Non-Negative Risk Estimator

From only positive (P) and unlabeled (U) data, a binary classifier could be trained with PU learning. Unbiased PU learning that is based on unbiased risk estimators is now state of the art. However, if its model is very flexible, its empirical risk on training data will go negative, and we will suffer from overfitting seriously. In this paper, we propose a… CONTINUE READING

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