Improving scalability of inductive logic programming via pruning and best-effort optimisation

•Pruning in hypothesis generalization algorithm enables learning from larger dataset.•Using latest optimization methods for better usage of modern solver technology.•Adding a time budget allowing the usage of suboptimal results in XHAIL.•Obtaining competitive results and explainable hypotheses in se...

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Vydané v:	Expert systems with applications Ročník 87; s. 291 - 303
Hlavní autori:	Kazmi, Mishal, Schüller, Peter, Saygın, Yücel
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Elsevier Ltd 30.11.2017 Elsevier BV
Predmet:	Answer Set Programming Artificial intelligence Chunking Datasets Inductive logic programming Logic programming Machine learning Natural Language Processing Optimization Pruning Statistical methods Chunking Inductive logic programming Answer Set Programming Natural Language Processing
ISSN:	0957-4174, 1873-6793
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