Fast relational learning using bottom clause propositionalization with artificial neural networks

Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis. Inductive Logic Programming (ILP) performs relational learning either directly by manipulating first-...

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Veröffentlicht in:	Machine learning Jg. 94; H. 1; S. 81 - 104
Hauptverfasser:	França, Manoel V. M., Zaverucha, Gerson, d’Avila Garcez, Artur S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.01.2014 Springer Nature B.V
Schlagworte:	Accuracy Artificial Intelligence Computer Science Control Learning Logic Logic programming Mathematical analysis Mechatronics Natural Language Processing (NLP) Neural networks Relational data bases Robotics Simulation and Modeling Tasks Vectors (mathematics) Relational learning Propositionalization Inductive logic programming Neural-symbolic integration Artificial neural networks
ISSN:	0885-6125, 1573-0565
Online-Zugang:	Volltext
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