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Inductive Logic Programming

An inductive logic programming framework to learn a concept from ambiguous examples

Dominique Bouthinon1, 2 Contact Information and Henry Soldano1, 2

(1)  Atelier de BioInformatique (ABI), Paris
(2)  Laboratoire d'Informatique Paris Nord (LIPN), Atelier de BioInformatique 12, rue Cuvier, 75005 Paris, France
Abstract
We address a learning problem with the following peculiarity : we search for characteristic features common to a learning set of objects related to a target concept. In particular we approach the cases where descriptions of objects are ambiguous : they represent several incompatible realities. Ambiguity arises because each description only contains indirect information from which assumptions can be derived about the object. We suppose here that a set of constraints allows the identification of ldquocoherentrdquo sub-descriptions inside each object.
We formally study this problem, using an Inductive Logic Programming framework close to characteristic induction from interpretations. In particular, we exhibit conditions which allow a pruned search of the space of concepts. Additionally we propose a method in which a set of hypothetical examples is explicitly calculated for each object prior to learning. The method is used with promising results to search for secondary substructures common to a set of RNA sequences.

Contact Information Dominique Bouthinon
Email: Dominique.Bouthinon@snv.jussieu.fr
Fax: (33) 0144 27 63 12
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