Tracking drifting concepts using random examples
David P. Helmbold, Philip M. Long · Conference on Learning Theory · 1991
In this paper we consider the problem of tracking a subset of the domain (called the target) which changes gradually over time. A single (unknown) probability distribution over the domain is used to generate random examples for the learning algorithm, measure the speed at which the target changes, and measure the error of the algorithm's hypothesis.