Streaming Machine Learning
Streaming Machine Learning is a machine learning paradigm that processes continuous, unbounded data streams in real time, distinguishing itself from batch learning by requiring incremental model updates under constraints of limited storage, single-pass data access, and concept drift adaptation.
766
Publications
44.9K
Citations
1.9K
Authors
735
Institutions
Publications per year
2017–2026
432
766
Learning from Time-Changing Data with Adaptive Windowing
Albert Bifet, Ricard Gavaldà · 2007 · 1.7K citations
A streaming ensemble algorithm (SEA) for large-scale classification
W. Nick Street, YongSeog Kim · 2001 · 1.2K citations
Learning under Concept Drift: A Review
IEEE Transactions on Knowledge and Data Engineering · 2018 · 873 citations · Full text
Learning in Nonstationary Environments: A Survey
Gregory Ditzler, Manuel Roveri, Cesare Alippi et al. · IEEE Computational Intelligence Magazine · 2015 · 784 citations · Full text
1–5 of 766
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735
University of Technology Sydney
Sydney, Australia
23 h-index
77 publications
3.4K citations

University of Waikato
Hamilton, New Zealand
21 h-index
62 publications
7.1K citations

Universidade do Porto
Porto, Portugal
20 h-index
37 publications
3K citations

Hefei University of Technology
Hefei, China
18 h-index
48 publications
3.5K citations

Télécom Paris
Palaiseau, France
18 h-index
34 publications
4.3K citations

University of Vermont
Burlington, United States
15 h-index
19 publications
1.3K citations
Virginia Commonwealth University
Richmond, United States
15 h-index
34 publications
2.1K citations
Universidade de São Paulo
São Paulo, Brazil
14 h-index
38 publications
2.3K citations
1–8 of 735
25 publications · 1.8K citations
22
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TKDE · 22 publications · 2K citations
20
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22 publications · 1.1K citations
19
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18 publications · 875 citations
17
h-index
Repository · 30 publications · 993 citations
15
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