Title: Machine learning for constraint acquisition
Authors: De Raedt, Luc
Issue Date: Sep-2014
Conference: ICON Summer School on Constraint Programming Meets Data Mining location:Sampieri, Sicily date:1-5 September 2014
Abstract: To use constraint programming, one needs to formulate a model that consists of a set of constraints.
Acquiring and formulating these constraints is a non-trivial task, in which one typically has to interact and collaborate intensively with a domain expert.
An alternative for eliciting the constraints from the expert is to use possibly available data sets in order to automatically learn the constraints
or mine for patterns that could help identify the right constraints.
This tutorial will survey a number of basic machine learning methods for learning constraints, it will make the link to computational learning theory
and will also use this to position existing work on automated learning of constraints in this context.
Description: Tutorial
Publication status: published
KU Leuven publication type: DI
Appears in Collections:Informatics Section

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