Training Population Optimization

Training Population Optimization is a methodological framework within training population design that systematically enhances the selection, composition, and structuring of training data to maximize model performance, generalizability, and learning efficiency under specified constraints.

29

Publications

2K

Citations

133

Authors

85

Institutions

Publications per year

2017–2026

18

Authors

133

  1. DA
    Deniz Akdemir

    Cornell University, United States

    5 publications · 640 citations

    5

    h-index

  2. JI
    Julio Isidro y Sánchez

    Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Spain

    4 publications · 607 citations

    4

    h-index

  3. JJ
    Jean‐Luc Jannink

    Cornell University, United States

    4 publications · 655 citations

    4

    h-index

  4. ME
    Mark E. Sorrells

    Cornell University, United States

    3 publications · 626 citations

    3

    h-index

  5. AH
    Ahmad H. Sallam

    University of Minnesota, United States

    2 publications · 50 citations

    2

    h-index

1–5 of 133

Venues

  1. Theoretical and Applied Genetics

    5 publications · 660 citations

    5

    h-index

  2. G3 Genes Genomes Genetics

    4 publications · 167 citations

    4

    h-index

  3. Crop Science

    2 publications · 232 citations

    2

    h-index

  4. Neurocomputing

    2 publications · 140 citations

    2

    h-index

  5. Frontiers in Genetics

    2 publications · 68 citations

    2

    h-index

1–5