Information, Prediction, and Query by Committee

We analyze the "query by committee" algorithm, a method for filtering informativequeries from a random stream of inputs. We show that if the two-member committee algorithm achieves information gainwith positive lower bound, then the prediction error decreases exponentially with the number of queries. We show that, in particular, this exponential decrease holds for query learning of thresholded smooth functions.