Rules and Similarity in Concept Learning

This paper argues that two apparently distinct modes of generalizing concepts abstracting rules and computing similarity to exemplars should both be seen as special cases of a more general Bayesian learning framework. Bayes explains the specific workings of these two modes which rules are abstracted, how similarity is measured as well as why generalization should appear ruleor similarity-based in different situations. This analysis also suggests why the rules/similarity distinction, even if not computationally fundamental, may still be useful at the algorithmic level as part of a principled approximation to fully Bayesian learning.