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Scaling Up Inductive Logic Programming by Learning From Interpretations

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Marcadores Sociales
Scaling Up Inductive Logic Programming by Learning From Interpretations
Id. 46430311
Idioma inglés
Titulo Scaling Up Inductive Logic Programming by Learning From Interpretations
Autor(es) Hendrik Blockeel
Hendrik Blockeel
Luc De Raedt
Luc De Raedt
Nico Jacobs
Nico Jacobs
Bart Demoen
Bart Demoen
Localización http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.1065
Versión 1.0
Estado Final
Descripción When comparing inductive logic programming (ILP) and attributevalue learning techniques, there is a trade-off between expressive power and efficiency. Inductive logic programming techniques are typically more expressive but also less efficient. Therefore, the data sets handled by current inductive logic programming systems are small according to general standards within the data mining community. The main source of inefficiency lies in the assumption that several examples may be related to each other, so they cannot be handled independently. Within the learning from interpretations framework for inductive logic programming this assumption is unnecessary, which allows to scale up existing ILP algorithms. In this paper we explain this learning setting in the context of relational databases. We relate the setting to propositional data mining and to the classical ILP setting, and show that learning from interpretations corresponds to learning from multiple relations and thus...
Tipo application/postscript
Palabras clave Inductive logic programming
Tipo de recurso Texto Narrativo
Tipo de Interactividad Expositivo
Nivel de Interactividad muy bajo
Audiencia Estudiante
Profesor
Autor
Estructura Atomic
Coste no
Copyright
Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Formatos application/postscript
Requerimientos técnicos Browser: Any
Relación [IsBasedOn] http://www.cs.kuleuven.ac.be/publicaties/rapporten/cw/CW297.ps.gz
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Fecha de contribución 28-jul-2009
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