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dc.creatorNuno A. Fonseca
dc.creatorRicardo Rocha
dc.creatorRui Camacho
dc.creatorVítor Santos Costa
dc.description.abstractInductive Logic Programming (ILP) is a powerful and welldeveloped abstraction for multi-relational data mining techniques. However, ILP systems are not particularly fast, most of their execution time is spent evaluating the hypotheses they construct. The evaluation time needed to assess the quality of each hypothesis depends mainly on the number of examples and the theorem proving effort required to determine if an example is entailed by the hypothesis. We propose a technique that reduces the theorem proving effort to a bare minimum and stores valuable information to compute the number of examples entailed by each hypothesis (using a tree data structure). The information is computed only once (pre-compiled) per example. Evaluation of hypotheses requires only basic and efficient operations on trees. This proposal avoids re-computation of hypothesis value in theory-level search and cross-validation algorithms, whenever the same data set is used with different parameters. In an empirical evaluation the technique yielded considerable speedups.
dc.relation.ispartof6th Workshop on Multi-Relational Data Mining (MRDM 2007)
dc.subjectEngenharia do conhecimento, Engenharia electrotécnica, electrónica e informática
dc.subjectKnowledge engineering, Electrical engineering, Electronic engineering, Information engineering
dc.titleILP: Compute Once, Reuse Often
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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