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Cited by 382Data Stream Mining TechniquesMachine Learning and ELMDomain Adaptation and Few-Shot Learning

Incremental on-line learning: A review and comparison of state of the art algorithms

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Losing, Viktor, Hammer, Barbara, Wersing, Heiko·2018·Neurocomputing
DOI 10.1016/j.neucom.2017.06.084
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Crossref · 10.1016/j.neucom.2017.06.084

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  • Existing surveys on incremental learning methods are somewhat dated and lack in-depth analysis, especially for overcoming catastrophic forgetting.

    “However, the existing researches are somewhat dated and lack in-depth (thorough) analysis especially for overcoming CF.”

    From An Appraisal of Incremental Learning Methods (2020) · cites this paper as [18]

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References 104 · Cited by 382

Authors
Losing, V., Hammer, B., & Wersing, H.
Affiliations
  • Bielefeld University (DE)
  • Honda (Germany) (DE)
Corresponding author
Losing, Viktor
ORCID
Venue
Neurocomputing
Publisher
Elsevier BV
Published
2018-01
Volume
275
Pages
1261-1274
ISSN
0925-2312 (ISSN-L)
DOI
10.1016/j.neucom.2017.06.084
OpenAlex
W2758219826
Cited by
382
References
104
Citations by year
2026: 37 · 2025: 34 · 2024: 35 · 2023: 61 · 2022: 47 · 2021: 48 · 2020: 51 · 2019: 52 · 2018: 16 · 2017: 1
FWCI
19.59
Type
journal article
Language
en
Open access
Licence
Locations
  • Neurocomputing (publishedVersion)
  • PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion, other-oa)
  • Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion, other-oa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
incremental learning, online learning, data streams, big data, online classification, hyperparameter optimization, algorithm comparison, convergence rate, model complexity, asymptotic behavior

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