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Learning from Highly Structured Data by Decomposition

R. MacKinney-Romero, C. Giraud-Carrier, Learning from Highly Structured Data by Decomposition. Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery (PKDD99). ISBN 3-540-66490-4, pp. 436–441. September 1999. PDF, 151 Kbytes.

Abstract

This paper addresses the problem of learning from highly structured data. Specifically, it describes a procedure, called decomposition, that allows a learner to access automatically the subparts of examples represented as closed terms in a higher-order language. This procedure maintains a clear distinction between the structure of an individual and its properties. A learning system based on decomposition is also presented and several examples of its use are described.

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