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Probabilistic Inductive Logic Programming on the Web
2017Probabilistic Inductive Logic Programming (PILP) is gaining attention for its capability of modeling complex domains containing uncertain relationships among entities. Among PILP systems, cplint provides inference and learning algorithms competitive with the state of the art. Besides parameter learning, cplint provides one of the few structure learning
RIGUZZI, Fabrizio +2 more
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Challenges for Inductive Logic Programming
1999Inductive logic programming (ILP) is a research area that has its roots in inductive machine learning and logic programming. Computational logic has significantly influenced machine learning through the field of inductive logic programming (ILP) which is concerned with the induction of logic programs from examples and background knowledge.
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Approaches to inductive logic programming
1992Inductive Logic Programming (ILP) is concerned with construction of logic programs from examples. It shares many concerns of Machine Learning (ML), but is committed to logic. As logic can help to provide a basis for elaborating such a methodology for learning, the area of ILP has attracted a wide attention of many researchers.
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A learning-based ontology alignment approach using inductive logic programming
Expert systems with applications, 2019Hamed Karimi, A. Kamandi
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Inductive Logic Programming Meets Relational Databases: Efficient Learning of Markov Logic Networks
International Conference on Inductive Logic Programming, 2016M. Malec +4 more
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QuickFOIL: Scalable Inductive Logic Programming
Proceedings of the VLDB Endowment, 2014Qiang Zeng, J. Patel, David Page
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Gallium nitride-based complementary logic integrated circuits
Nature Electronics, 2021Zheyang Zheng, Li Zhang, Han Xu
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