Quality Loss Minimization

Quality Loss Minimization is a methodological framework within loss reduction that specifically aims to reduce performance degradation in machine learning models by optimizing the discrepancy between predicted and true quality levels during training.

228

Publications

9.9K

Citations

639

Authors

270

Institutions

Publications per year

2017–2026

75

Authors

639

  1. AJ
    Angus Jeang

    Feng Chia University, Taiwan

    9 publications · 248 citations

    9

    h-index

  2. CC
    Chung‐Ho Chen

    Southern Taiwan University of Science and Technology, Taiwan

    5 publications · 80 citations

    5

    h-index

  3. CW
    Chien‐Wei Wu

    National Tsing Hua University, Taiwan

    4 publications · 84 citations

    4

    h-index

  4. BT
    Bradley Thwaites

    Georgia Institute of Technology, United States

    4 publications · 136 citations

    4

    h-index

  5. HE
    Hadi Esmaeilzadeh

    Georgia Institute of Technology, United States

    4 publications · 136 citations

    4

    h-index

1–5 of 639

Venues

  1. International Journal of Production Research

    17 publications · 531 citations

    16

    h-index

  2. 10

    h-index

  3. 10

    h-index

  4. 5

    h-index

  5. Quality Engineering

    5 publications · 95 citations

    5

    h-index

1–5