Part of Advances in Neural Information Processing Systems 9 (NIPS 1996)
David Cohn
I describe a querying criterion that attempts to minimize the error of a learner by minimizing its estimated squared bias. I describe experiments with locally-weighted regression on two simple prob(cid:173) lems, and observe that this "bias-only" approach outperforms the more common "variance-only" exploration approach, even in the presence of noise.