A typical active learner begins with a small labeled set L, selects one or more informative query instances from a large unlabeled pool U, learns from these labeled queries (which are then added to L), and repeats.
this can be thought of as a QBC generalization of N -best entropy, where each committee member casts a vote for the posterior label distribution.