Learning from Highly Structured Data by Decomposition

This paper addresses the problem of learning from highly structured data. Speci cally, 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.

• Type: Conference
• Year: 1999
• Where: PKDD
• Authors: René MacKinney-Romero, Christophe G. Giraud-Carrier
• Keywords: Clear Distinction, Closed Terms, Data Mining, Learner, PKDD 1999