Semi-supervised online structure learning for composite event recognition

Online structure learning approaches, such as those stemming from statistical relational learning, enable the discovery of complex relations in noisy data streams. However, these methods assume the existence of fully-labelled training data, which is unrealistic for most real-world applications. We p...

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Vydané v:	Machine learning Ročník 108; číslo 7; s. 1085 - 1110
Hlavní autori:	Michelioudakis, Evangelos, Artikis, Alexander, Paliouras, Georgios
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.07.2019 Springer Nature B.V
Predmet:	Artificial Intelligence Caching Computer Science Control Data transmission Distance learning Human activity recognition Labels Mechatronics Natural Language Processing (NLP) Optimization Robotics Simulation and Modeling Special Issue of the Inductive Logic Programming (ILP) 2017-2018 Graph-cut minimisation First-order logic distance Event recognition Semi-supervised learning Event Calculus Online structure learning
ISSN:	0885-6125, 1573-0565
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