Active Learning For Filtering

Active Learning for Filtering is an information filtering methodology that selectively queries the most informative instances for labeling in order to optimize model performance with minimal human annotation effort.

40

Publications

2.3K

Citations

115

Authors

41

Institutions

Publications per year

2017–2026

12

Authors

115

  1. DS
    D. Sculley

    Google (United States), United States

    4 publications · 135 citations

    4

    h-index

  2. RV
    Rens van de Schoot

    Utrecht University, Netherlands

    1 publications · 84 citations

    2

    h-index

  3. YY
    Yiming Yang

    Carnegie Mellon University, United States

    2 publications · 133 citations

    2

    h-index

  4. AH
    Abhay Harpale

    Carnegie Mellon University, United States

    2 publications · 133 citations

    2

    h-index

  5. AK
    Aris Kosmopoulos

    National Centre of Scientific Research "Demokritos", Greece

    2 publications · 33 citations

    2

    h-index

1–5 of 115

Venues

  1. Systematic Reviews

    1 publications · 84 citations

    2

    h-index

  2. Text REtrieval Conference

    Conference · 1 publications · 20 citations

    1

    h-index

  3. ACM Transactions on Software Engineering and Methodology

    TOSEM · 1 publications · 33 citations

    1

    h-index

  4. Electric Power Systems Research

    1 publications · 54 citations

    1

    h-index

  5. Computer Science Review

    1 publications · 300 citations

    1

    h-index

1–5