FFNSL: Feed-Forward Neural-Symbolic Learner

Logic-based machine learning aims to learn general, interpretable knowledge in a data-efficient manner. However, labelled data must be specified in a structured logical form. To address this limitation, we propose a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FFN...

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Vydané v:	Machine learning Ročník 112; číslo 2; s. 515 - 569
Hlavní autori:	Cunnington, Daniel, Law, Mark, Lobo, Jorge, Russo, Alessandra
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.02.2023 Springer Nature B.V
Predmet:	Artificial Intelligence Computer Science Control Impact analysis Logic Machine Learning Mechatronics Natural Language Processing (NLP) Neural networks Robotics Simulation and Modeling Special issue on Learning and Reasoning Unstructured data Neural-symbolic learning Inductive logic programming Distributional shift Logic-based machine learning
ISSN:	0885-6125, 1573-0565
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