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.
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Bouthinon, D., Soldano, H. (1998). An inductive logic programming framework to learn a concept from ambiguous examples. In: Nédellec, C., Rouveirol, C. (eds) Machine Learning: ECML-98. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0026694
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DOI: https://doi.org/10.1007/BFb0026694
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