# PU learning

In PU learning, two sets of samples are assumed to be available for training: the positive set $P$ and a mixed set $U$, which is assumed to contain both positive and negative samples, but without these being labeled as such. This contrasts with other forms of semisupervised learning, where it is assumed that a labeled set containing examples of both classes is available. A variety of techniques exist to adapt supervised classifiers to the PU learning setting. PU learning successfully been applied to text classification [2][3][4] and bioinformatics tasks.[5]