Label Size Optimization

Label Size Optimization is a methodological contribution within the field of labeling control that systematically determines the minimal yet sufficient number of labels required to achieve optimal classification or annotation performance, distinguishing itself from broader labeling strategies by its focused emphasis on balancing label efficiency and model effectiveness.

16

Publications

746

Citations

37

Authors

20

Institutions

Publications per year

2017–2026

1

Authors

37

  1. AK
    Amos Korman

    Centre National de la Recherche Scientifique, France

    3 publications · 73 citations

    3

    h-index

  2. AW
    Ada Wai-Chee Fu

    Chinese University of Hong Kong, Hong Kong SAR China

    2 publications · 137 citations

    2

    h-index

  3. CG
    Cyril Gavoille

    Université de Bordeaux, France

    1 publications · 11 citations

    1

    h-index

  4. JL
    José L. Marzo

    Universitat de Girona, Spain

    1 publications · 23 citations

    1

    h-index

  5. HP
    Holger Petersen

    University of Stuttgart, Germany

    1 publications · 11 citations

    1

    h-index

1–5 of 37

Venues

  1. Symposium on Discrete Algorithms

    Conference · 1 publications · 156 citations

    1

    h-index

  2. IEEE/ACM Transactions on Networking

    TON · 1 publications · 23 citations

    1

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 22 citations

    1

    h-index

  4. IEEE Micro

    1 publications · 17 citations

    1

    h-index

  5. Procedia Computer Science

    1 publications · 12 citations

    1

    h-index

1–5