Abstract

In this paper, we propose a probabilistic framework for memory-based reasoning (MBR). The framework allows us to clarify the technical merits and limitations of several recently published MBR methods and to design new variants. The proposed computational framework consists of
three components: a specification language to define an adaptive notion of relevant context for a query; mechanisms for retrieving this context; and local learning procedures that are used to induce the desired action from this context. We primarily focus on actions in the form of a classification. Based on the framework we derive several analytical and empirical results that shed light on MBR algorithms. We introduce the notion of an MBR transform, and discuss its utility for learning algorithms. We also provide several perspectives on memory-based reasoning from a multi-disciplinary point of view. © 1998 Published by Elsevier Science B.V.

Keywords:  Learning: Probabilistic inference; Meta-learning; Local learning: MBR transform; Memory-based learning; Bayes networks