Active learning by querying informative and representative examples

Most active learning approaches select either informative or representative unla-beled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for query selection, they are usu-ally ad hoc in finding unlabeled instances that are both informative and repre-sentative. We address this challenge by a principled approach, termed QUIRE, based on the min-max view of active learning. The proposed approach provides a systematic way for measuring and combining the informativeness and represen-tativeness of an instance. Extensive experimental results show that the proposed QUIRE approach outperforms several state-of-the-art active learning approaches.