A PROBABILISTIC FRAMEWORK FOR MEMORY-BASED REASONING

In this paper, we propose a probabilistic framework for memory-based r easoning (MBR). The framework allows us to clarify the technical merit s and limitations of several recently published MBR methods and to des ign new variants. The proposed computational framework consists of thr ee components: a specification language to define an adaptive notion o f relevant context for a query; mechanisms for retrieving this context ; and local learning procedures that are used to induce the desired ac tion from this context. We primarily focus on actions in the form of a classification. Based on the framework we derive several analytical a nd empirical results that shed light on MBR algorithms. We introduce t he notion of an MBR transform, and discuss its utility for learning al gorithms. We also provide several perspectives on memory-based reasoni ng from a multi-disciplinary point of view. (C) 1998 Published by Else vier Science B.V.