Two contributions of constraint programming to machine learning

Authors:
Arnaud Lallouet;Andreï Legtchenko
Affiliations:
Université d'Orléans — LIFO, Orléans;Université d'Orléans — LIFO, Orléans
Venue:
ECML'05 Proceedings of the 16th European conference on Machine Learning
Year:
2005

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Abstract

A constraint is a relation with an active behavior. For a given relation, we propose to learn a representation adapted to this active behavior. It yields two contributions. The first is a generic meta-technique for classifier improvement showing performances comparable to boosting. The second lies in the ability of using the learned concept in constraint-based decision or optimization problems. It opens a new way of integrating Machine Learning in Decision Support Systems.