On the Importance of Change of Representation in Induction

Abstract

The adequacy of current machine-learning techniques relies on an implicit built-in assumption in which simplicity and similarity play significant roles. This paper discusses on the insufficiency of these assumptions for domains where complex attribute interactions characterize the target concept (i.e. on difficult domains), and revives a model of induction where change of representation, through the construction of increasingly more abstract knowledge structures, contributes to increased learning performance. We describe two learning systems proved effective on difficult domains, their success attributed to the use of knowledge structures that bridge the gap between primitive and complex representations. Our conclusions emphasize the importance of change of representation as a fundamental step during inductive learning. Background: Success and failure in inductive learning Past successes and failures of learning techniques have been conditioned by an assumption implicit in a model of..

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