Pool-based Active Learning

Pool-Based Active Learning is a type of active learning paradigm in which the algorithm selects the most informative instances from a fixed, pre-existing pool of unlabeled data for annotation, thereby distinguishing itself from streaming or query-synthesis approaches by its reliance on a static candidate set.

95

Publications

5.2K

Citations

260

Authors

132

Institutions

Publications per year

2017–2026

42

Authors

260

  1. DW
    Dongrui Wu

    Huazhong University of Science and Technology, China

    2 publications · 187 citations

    3

    h-index

  2. ÓR
    Óscar Reyes

    University of Córdoba, Spain

    2 publications · 136 citations

    2

    h-index

  3. SA
    Stéphane Ayache

    Centre National de la Recherche Scientifique, France

    2 publications · 75 citations

    2

    h-index

  4. SS
    Sivan Sabato

    Ben-Gurion University of the Negev, Israel

    2 publications · 29 citations

    2

    h-index

  5. AO
    Antonio Ortega

    University of Southern California, United States

    2 publications · 148 citations

    2

    h-index

1–5 of 260

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 349 citations

    9

    h-index

  2. Machine Learning

    2 publications · 185 citations

    3

    h-index

  3. Neurocomputing

    3 publications · 112 citations

    3

    h-index

  4. Information Sciences

    3 publications · 93 citations

    3

    h-index

  5. 2

    h-index

1–5