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25

PKDD
1999
Springer

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Learning from Highly Structured Data by Decomposition

14 years 3 months ago

Learning from Highly Structured Data by Decomposition

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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.

René MacKinney-Romero, Christophe G. Giraud

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Clear Distinction | Closed Terms | Data Mining | Learner | PKDD 1999 |

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Added	04 Aug 2010
Updated	04 Aug 2010
Type	Conference
Year	1999
Where	PKDD
Authors	René MacKinney-Romero, Christophe G. Giraud-Carrier

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