A formal model of hierarchical concept-learning

Abstract

We show how to learn from examples (Valiant style) any concept representable as a boolean function or circuit, with the help of a teacher who breaks the concept into subconcepts and teaches one subconcept per lesson. Each subconcept corresponds to a gate in the boolean circuit. The learner learns each subconcept from examples which have been randomly drawn according to an arbitrary probability distribution, and labeled as positive or negative instances of the subconcept by the teacher. The learning procedure runs in time polynomial in the size of the circuit. The learner outputs not the unknown boolean circuit, but rather a program that, for any input, either produces the same answer as the unknown boolean circuit, or else says “I don’t know.” Thus the output of this learning procedure is reliable. Furthermore, with high probability the output program is nearly always useful in that it says “I don’t know” very rarely. A key technique is to maintain a hierarchy of explicit “version spaces.” Our main contribution is thus a learning procedure whose output is reliable and nearly always useful; this has not been previously accomplished within Valiant’s model of learnability. © 1994 Academic Press, Inc.