In many real-world problems, unlabeled data are often abundant whereas labeled data are scarce. Label acquisition is usually expensive due to the involvement of human experts, and thus, it is important to train an accurate prediction model by a small number of labeled instances. Active learning aims at reducing human efforts on annotating examples in a machine learning system, and has been successfully applied into various real tasks.
