Information, Prediction, and Query by Committee
Yoav Freund, H. Sebastian Seung, Eli Shamir, Naftali Tishby · Neural Information Processing Systems · 1992
We analyze the by algorithm, a method for filtering informative queries from a random stream of inputs. We show that if the two-member committee algorithm achieves information gain with 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.