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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Veröffentlicht in:Machine learning Jg. 108; H. 7; S. 1085 - 1110
Hauptverfasser: Michelioudakis, Evangelos, Artikis, Alexander, Paliouras, Georgios
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.07.2019
Springer Nature B.V
Schlagworte:
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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Zusammenfassung: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 present a novel approach for completing the supervision of a semi-supervised structure learning task. We incorporate graph-cut minimisation, a technique that derives labels for unlabelled data, based on their distance to their labelled counterparts. In order to adapt graph-cut minimisation to first order logic, we employ a suitable structural distance for measuring the distance between sets of logical atoms. The labelling process is achieved online (single-pass) by means of a caching mechanism and the Hoeffding bound, a statistical tool to approximate globally-optimal decisions from locally-optimal ones. We evaluate our approach on the task of composite event recognition by using a benchmark dataset for human activity recognition, as well as a real dataset for maritime monitoring. The evaluation suggests that our approach can effectively complete the missing labels and eventually, improve the accuracy of the underlying structure learning system.
Bibliographie:ObjectType-Article-1
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ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-019-05794-2