Lifted discriminative learning of probabilistic logic programs

Probabilistic logic programming (PLP) provides a powerful tool for reasoning with uncertain relational models. However, learning probabilistic logic programs is expensive due to the high cost of inference. Among the proposals to overcome this problem, one of the most promising is lifted inference. I...

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Veröffentlicht in:	Machine learning Jg. 108; H. 7; S. 1111 - 1135
Hauptverfasser:	Nguembang Fadja, Arnaud, Riguzzi, Fabrizio
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.07.2019 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Computer Science Control Inference Logic programming Logic programs Machine learning Mathematical models Mechatronics Natural Language Processing (NLP) Parameters Robotics Simulation and Modeling Software Special Issue of the Inductive Logic Programming (ILP) Expectation maximization Lifted inference Probabilistic inductive logic programming Statistical relational learning Probabilistic logic programming
ISSN:	0885-6125, 1573-0565
Online-Zugang:	Volltext
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