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

  • Dominique Bouthinon
  • Henry Soldano
Inductive Logic Programming
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1398)

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 “coherent” 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.

Keywords

Integrity Constraint Inductive Logic Target Concept Inductive Logic Programming Domain Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Dominique Bouthinon
    • 1
    • 2
  • Henry Soldano
    • 1
    • 2
  1. 1.Atelier de BioInformatique (ABI)Paris
  2. 2.Laboratoire d'Informatique Paris Nord (LIPN)Atelier de BioInformatique 12ParisFrance

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