Overfitting Detection

Overfitting Detection is a methodological contribution in pattern evaluation that identifies when a model's performance on training data significantly exceeds its performance on unseen data due to excessive complexity or overadaptation to training noise.

236

Publications

29.5K

Citations

767

Authors

346

Institutions

Publications per year

2017–2026

153

Authors

767

  1. RR
    Rebecca Roelofs

    Google (United States), United States

    3 publications · 249 citations

    3

    h-index

  2. SW
    Shuihua Wang‎

    University of Leicester, United Kingdom

    3 publications · 153 citations

    3

    h-index

  3. ZR
    Zeyu Ren

    University of Leicester, United Kingdom

    3 publications · 153 citations

    3

    h-index

  4. XD
    Xuan-Bach D. Le

    Singapore Management University, Singapore

    3 publications · 71 citations

    3

    h-index

  5. LK
    Leonid Kogan

    Massachusetts Institute of Technology, United States

    2 publications · 58 citations

    2

    h-index

1–5 of 767

Venues

  1. arXiv (Cornell University)

    Repository · 22 publications · 1.5K citations

    16

    h-index

  2. IEEE Access

    5 publications · 546 citations

    5

    h-index

  3. IEEE Transactions on Neural Networks

    4 publications · 543 citations

    4

    h-index

  4. Applied Sciences

    4 publications · 126 citations

    4

    h-index

  5. Journal of Physics Conference Series

    3 publications · 2.3K citations

    3

    h-index

1–5