A Bayesian Framework for Concept Learning

Joshua B. Tenenbaum

Abstract This paper develops a rational computational analysis of the problem of learning a concept from a small number of positive examples. Despite its basic importance, this learning situation has been relatively ignored by formal modelers in both cognitive psychology and machine learning. The Bayesian learning framework presented here provides a principled approach to fundamental questions of inductive inference that have puzzled many philosophers but few children, such as how far and in what ways to generalize a concept beyond the examples encountered. This theory may lead to a better understanding of human category learning, as well as to machine learning algorithms that can learn from a human user's examples the way other humans can.