Guest Editorial: Special Issue on Stream Learning

Lu, J; Gama, J; Yao, X; Minku, L

Guest Editorial: Special Issue on Stream Learning

Lu, J

Gama, J Yao, X Minku, L

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Neural Networks and Learning Systems, 2023, 34, (10), pp. 6683-6685
Issue Date:: 2023-10-01

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Field	Value	Language
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.contributor.author	Gama, J
dc.contributor.author	Yao, X
dc.contributor.author	Minku, L
dc.date.accessioned	2024-02-07T00:49:18Z
dc.date.available	2024-02-07T00:49:18Z
dc.date.issued	2023-10-01
dc.identifier.citation	IEEE Transactions on Neural Networks and Learning Systems, 2023, 34, (10), pp. 6683-6685
dc.identifier.issn	2162-237X
dc.identifier.issn	2162-2388
dc.identifier.uri	http://hdl.handle.net/10453/175415
dc.description.abstract	In recent years, learning from streaming data, commonly known as stream learning, has enjoyed tremendous growth and shown a wealth of development at both the conceptual and application levels. Stream learning is highly visible in both the machine learning and data science fields and has become a hot new direction in research. Advancements in stream learning include learning with concept drift detection, that includes whether a drift has occurred; understanding where, when, and how a drift occurs; adaptation by actively or passively updating models; and online learning, active learning, incremental learning, and reinforcement learning in data streaming situations.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Neural Networks and Learning Systems
dc.relation.isbasedon	10.1109/TNNLS.2023.3304146
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Guest Editorial: Special Issue on Stream Learning
dc.type	Journal Article
utslib.citation.volume	34
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2024-02-07T00:49:17Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	34
utslib.citation.issue	10

Abstract:

In recent years, learning from streaming data, commonly known as stream learning, has enjoyed tremendous growth and shown a wealth of development at both the conceptual and application levels. Stream learning is highly visible in both the machine learning and data science fields and has become a hot new direction in research. Advancements in stream learning include learning with concept drift detection, that includes whether a drift has occurred; understanding where, when, and how a drift occurs; adaptation by actively or passively updating models; and online learning, active learning, incremental learning, and reinforcement learning in data streaming situations.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/175415