Abstract
In this paper, we present a system, called icc, that learns constrained logic programs containing function symbols. The particularity of our approach is to consider, as in the field of Constraint Logic Programming, a specific computation domain and to handle terms by taking into account their values in this domain. Nevertheless, an earlier version of our system was only able to learn constraints X i=t, where X i is a variable and t is a term. We propose here a method for learning linear constraints. It has already been a lot studied in the field of Statistical Learning Theory and for learning Oblic Decision Trees. As far as we know, the originality of our approach is to rely on a Linear Programming solver. Moreover, integrating it in icc enables to learn non linear constraints.
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© 1997 Springer-Verlag Berlin Heidelberg
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Martin, L., Vrain, C. (1997). Learning Linear Constraints in Inductive Logic Programming. In: van Someren, M., Widmer, G. (eds) Machine Learning: ECML-97. ECML 1997. Lecture Notes in Computer Science, vol 1224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62858-4_81
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DOI: https://doi.org/10.1007/3-540-62858-4_81
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