Training Set Size Optimization

Training Set Size Optimization is a methodological framework within training population design that systematically determines the minimal data sample size required to achieve desired model performance, distinguishing itself from related methods by focusing explicitly on the trade-off between dataset scale and learning efficiency.

176

Publications

13K

Citations

595

Authors

282

Institutions

Publications per year

2017–2026

75

Authors

595

  1. NK
    Naimul Khan

    Toronto Metropolitan University, Canada

    4 publications · 452 citations

    4

    h-index

  2. MH
    Marcia Hon

    Toronto Metropolitan University, Canada

    4 publications · 452 citations

    4

    h-index

  3. DP
    Daniel P. W. Ellis

    Columbia University, United States

    3 publications · 2.5K citations

    3

    h-index

  4. XZ
    Xiaojin Zhu

    University of Wisconsin–Madison, United States

    2 publications · 22 citations

    2

    h-index

  5. MB
    Miloš Božić

    University of Nis, Serbia

    2 publications · 39 citations

    2

    h-index

1–5 of 595

Venues

  1. arXiv (Cornell University)

    Repository · 8 publications · 102 citations

    8

    h-index

  2. Theoretical and Applied Genetics

    4 publications · 177 citations

    4

    h-index

  3. PLoS ONE

    4 publications · 94 citations

    4

    h-index

  4. 4

    h-index

  5. G3 Genes Genomes Genetics

    3 publications · 373 citations

    3

    h-index

1–5