An Analysis of Representation Shift in Concept Learning

Abstract

In spite of the importance of representation in learning, little progress has been made toward understanding what makes representations work. This paper describes a framework for knowledge-level analysis of changes in the representation of training examples in concept learning. This a very fundamental sort of representation change; such a change alters the very space over which learning occurs, find hence necessitates selection of a new hypothesis space and (probably) a new learning algorithm. The goals of this paper are first, to provide a framework for analysis of representation shifts; second, to make explicit the assumptions implicit in representation shifts that have actually been used in learning systems; and third, to suggest a procedure for finding the most appropriate representation shift, given some background knowledge about a learning problem. The analytic framework is used to analyze a class of hybrid EBL/SBL systems by characterizing the sorts of domain theories that can be used with these systems.