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CORR
2000
Springer

Scaling Up Inductive Logic Programming by Learning from Interpretations

10 years 2 months ago
Scaling Up Inductive Logic Programming by Learning from Interpretations
When comparing inductive logic programming (ILP) and attribute-value 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 extends the e...
Hendrik Blockeel, Luc De Raedt, Nico Jacobs, Bart
Added 17 Dec 2010
Updated 17 Dec 2010
Type Journal
Year 2000
Where CORR
Authors Hendrik Blockeel, Luc De Raedt, Nico Jacobs, Bart Demoen
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