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Cited by 651Machine Learning and Data ClassificationMachine Learning and AlgorithmsDomain Adaptation and Few-Shot Learning

Learning from positive and unlabeled data: a survey

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Bekker, Jessa, Davis, Jesse·2020·Machine Learning
DOI 10.1007/s10994-020-05877-5
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Cited by

Sorted by citations

Authors
Bekker, J., & Davis, J.
Affiliations
KU Leuven (BE)
Corresponding author
Bekker, Jessa
ORCID
Bekker, Jessa
Venue
Machine Learning
Publisher
Springer Science and Business Media LLC
Published
2020-04
Volume
109
Issue
4
Pages
719-760
ISSN
  • 0885-6125 (ISSN-L)
  • 1573-0565
DOI
10.1007/s10994-020-05877-5
OpenAlex
W2899867782
Cited by
651
References
3
Citations by year
2026: 109 · 2025: 117 · 2024: 117 · 2023: 107 · 2022: 85 · 2021: 70 · 2020: 33 · 2019: 12 · 2018: 1
FWCI
28.62
Type
journal article
Language
en
Open access
Licence
Locations
  • Machine Learning (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Lirias (submittedVersion, other-oa)
  • arXiv (Cornell University)
Funders
  • KU Leuven (C/14/17/070, C22/15/015, C32/17/036)
  • Fonds Wetenschappelijk Onderzoek (SBO-150033, EOS-30992574)
  • Interreg (V A project NANO4Sports)
  • Agentschap voor Innovatie door Wetenschap en Technologie (SB/141744)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
positive-unlabeled learning, semi-supervised learning, unlabeled data, negative examples, diagnosis, knowledge base completion

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