Novel approaches for flexible supervision KU Leuven
For binary classification, standard supervised learning algorithms require fully labeled training data which contains both positive and negative examples. However, in many settings a learner only does not have access to such data. For example, the data may consist of a (small) set of positive examples and a large set of unlabeled examples, where the unlabeled data contains a mix of both positive and negative examples. This type of data is ...