Anomaly detection methods aim to identify examples that do not follow the expected behavior. For various reasons, anomaly detection is typically tackled by using unsupervised approaches that assign real-valued anomaly scores based on various …
In the multi-instance learning (MIL) setting instances are grouped together into bags. Labels are provided only for the bags and not on the level of individual instances. A positive bag label means that at least one instance inside the bag is …
Estimating the proportion of positive examples (i.e., the class prior) from positive and unlabeled (PU) data is an important task that facilitates learning a classifier from such data. In this paper, we explore how to tackle this problem when the …