Learning From Snapshot Examples

Abstract

Examples are a powerful tool for teaching both humans and computers.In order to learn from examples, however, a student must first extractthe examples from its stream of perception. Snapshot learning is ageneral approach to this problem, in which relevant samples ofperception are used as examples. Learning from these examples can inturn improve the judgement of the snapshot mechanism, improving thequality of future examples. One way to implement snapshot learning isthe Top-Cliff heuristic, which identifies relevant samples using ageneralized notion of peaks. I apply snapshot learning with theTop-Cliff heuristic to solve a distributed learning problem and showthat the resulting system learns rapidly and robustly, and canhallucinate useful examples in a perceptual stream from a teacherlesssystem.