—Active learning can actively select or construct examples to label to reduce the number of labeled examples needed for building accurate classifiers. However, previous works of...
We explore a general Bayesian active learning setting, in which the learner can ask arbitrary yes/no questions. We derive upper and lower bounds on the expected number of queries r...
Active learning may hold the key for solving the data scarcity problem in supervised learning, i.e., the lack of labeled data. Indeed, labeling data is a costly process, yet an ac...
It has been established that active learning is effective for learning complex, subjective query concepts for image retrieval. However, active learning has been applied in a conc...
We investigate the problem of active learning on a given tree whose nodes are assigned binary labels in an adversarial way. Inspired by recent results by Guillory and Bilmes, we c...