Small Sample Overfitting

Small Sample Overfitting is a type of overfitting that occurs when a model learns spurious patterns or noise from a limited training dataset, leading to poor generalization on unseen data due to insufficient statistical power to reliably estimate model parameters.

26

Publications

2.3K

Citations

115

Authors

50

Institutions

Publications per year

2017–2026

14

Authors

115

  1. ZM
    Zhanyu Ma

    Beijing University of Posts and Telecommunications, China

    2 publications · 68 citations

    2

    h-index

  2. JX
    Jing‐Hao Xue

    University College London, United Kingdom

    2 publications · 68 citations

    2

    h-index

  3. XL
    Xiaoxu Li

    Lanzhou University of Technology, China

    2 publications · 68 citations

    2

    h-index

  4. JG
    Jun Guo

    Beijing University of Posts and Telecommunications, China

    2 publications · 68 citations

    2

    h-index

  5. LK
    Luke Keele

    University of Pennsylvania, United States

    1 publications · 68 citations

    1

    h-index

1–5 of 115

Venues

  1. 1

    h-index

  2. PLoS ONE

    1 publications · 81 citations

    1

    h-index

  3. 1

    h-index

  4. Lecture notes in computer science

    LNCS · Book series · 1 publications · 15 citations

    1

    h-index

  5. Research Repository (Delft University of Technology)

    Repository · 1 publications · 11 citations

    1

    h-index

1–5