Active Learning For Classification

Active Learning for Classification is a machine learning paradigm within supervised classification that iteratively selects the most informative instances for labeling to improve model performance while minimizing annotation effort.

1.2K

Publications

70.6K

Citations

3K

Authors

958

Institutions

Publications per year

2017–2026

523

Authors

3K

  1. LB
    Lorenzo Bruzzone

    University of Trento, Italy

    13 publications · 1.1K citations

    12

    h-index

  2. DT
    Devis Tuia

    École Polytechnique Fédérale de Lausanne, Switzerland

    12 publications · 1.8K citations

    12

    h-index

  3. JW
    Jian Wu

    Soochow University, China

    10 publications · 228 citations

    10

    h-index

  4. JL
    Jun Li

    Sun Yat-sen University, China

    10 publications · 595 citations

    10

    h-index

  5. KG
    Kristen Grauman

    The University of Texas at Austin, United States

    10 publications · 1.1K citations

    10

    h-index

1–5 of 3K

Venues

  1. arXiv (Cornell University)

    Repository · 60 publications · 3.3K citations

    29

    h-index

  2. 26

    h-index

  3. Neurocomputing

    23 publications · 1.3K citations

    19

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 22 publications · 726 citations

    16

    h-index

  5. Pattern Recognition

    17 publications · 953 citations

    15

    h-index

1–5