Training Population Sampling

Training Population Sampling is a methodological construct in machine learning that specifies the systematic selection of instances from a target domain to form a representative training population, distinguishing itself from related sampling techniques by its focus on optimizing model generalization through strategic instance inclusion.

17

Publications

677

Citations

68

Authors

52

Institutions

Publications per year

2017–2026

7

Authors

68

  1. ME
    Mark E. Sorrells

    Cornell University, United States

    1 publications · 149 citations

    1

    h-index

  2. AP
    Andrey Parasich

    1 publications · 12 citations

    1

    h-index

  3. BJ
    Brian J. Ross

    Brock University, Canada

    1 publications · 12 citations

    1

    h-index

  4. FR
    Frances R. Thistlethwaite

    Scion, New Zealand

    1 publications · 37 citations

    1

    h-index

  5. BR
    Blaise Ratcliffe

    University of British Columbia, Canada

    1 publications · 37 citations

    1

    h-index

1–5 of 68

Venues

  1. Pattern Recognition

    2 publications · 22 citations

    2

    h-index

  2. Heredity

    1 publications · 37 citations

    1

    h-index

  3. Theoretical and Applied Genetics

    1 publications · 57 citations

    1

    h-index

  4. Crop Science

    1 publications · 149 citations

    1

    h-index

  5. G3 Genes Genomes Genetics

    1 publications · 69 citations

    1

    h-index

1–5