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dc.creatorRui Camacho
dc.description.abstractInductive Logic Programming (ILP) is a promising technol-ogy for knowledge extraction applications. ILP has produced intelligiblesolutions for a wide variety of domains where it has been applied. TheILP lack of eciency is, however, a major impediment for its scalabilityto applications requiring large amounts of data. In this paper we pro-pose a set of techniques that improve ILP systems eciency and makethen more likely to scale up to applications of knowledge extraction fromlarge datasets. We propose and evaluate the lazy evaluation of examples,to improve the eciency of ILP systems. Lazy evaluation is essentiallya way to avoid or postpone the evaluation of the generated hypotheses(coverage tests).The techniques were evaluated using the IndLog system on ILP datasetsreferenced in the literature. The proposals lead to substantial eficiencyimprovements and are generally applicable to any ILP system.
dc.relation.ispartof11th Portuguese Conference on Artificial Intelligence (EPIA 2003)
dc.subjectEngenharia de computadores, Engenharia electrotécnica, electrónica e informática
dc.subjectComputer engineering, Electrical engineering, Electronic engineering, Information engineering
dc.titleImproving the efficiency of ILP systems
dc.typeArtigo em Livro de Atas de Conferência Internacional
dc.contributor.uportoFaculdade de Engenharia
dc.subject.fosCiências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática
dc.subject.fosEngineering and technology::Electrical engineering, Electronic engineering, Information engineering
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

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