Theoretical foundations of active learning

Abstract

In active learning, a learning algorithm is given access to a large pool of unlabeled examples, and is allowed to request the label of any particular examples from that pool, interactively. The objective is to learn a function that accurately predicts the labels of new examples, while requesting as few labels as possible. This contrasts with passive learning, where the examples to be labeled are chosen randomly. In comparison, active learning can often significantly decrease