Active learning for data streams: a survey

Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world appl...

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Veröffentlicht in:	Machine learning Jg. 113; H. 1; S. 185 - 239
Hauptverfasser:	Cacciarelli, Davide, Kulahci, Murat
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.01.2024 Springer Nature B.V
Schlagworte:	Active learning Artificial Intelligence Availability Bandits Computer Science Concept drift Control Data points Data streams Data transmission Experimental design Kvalitetsteknik och logistik Labeling Labels Literature reviews Machine Learning Mechatronics Natural Language Processing (NLP) Online active learning Online learning Quality Technology and Logistics Query strategies Robotics Selective sampling Simulation and Modeling Stream-based active learning Unlabeled data Unlabeled data Query strategies Selective sampling Concept drift Experimental design Stream-based active learning Data streams Online active learning Online learning Bandits
ISSN:	0885-6125, 1573-0565, 1573-0565
Online-Zugang:	Volltext
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