Redundancy Estimation

Redundancy Estimation is a methodological framework within pruning intensity research that quantifies the degree of superfluous or non-essential components in a model structure to identify elements amenable to removal without significant performance degradation.

46

Publications

1.4K

Citations

122

Authors

69

Institutions

Publications per year

2017–2026

11

Authors

122

  1. BJ
    Byron J. T. Morgan

    University of Kent, United Kingdom

    2 publications · 51 citations

    2

    h-index

  2. CS
    Christian Scharfenberger

    University of Waterloo, Canada

    2 publications · 21 citations

    2

    h-index

  3. TC
    Terry C. Gleason

    University of Michigan, United States

    2 publications · 94 citations

    2

    h-index

  4. SK
    Somesh Kumar

    Indian Institute of Technology Kharagpur, India

    1 publications · 24 citations

    1

    h-index

  5. XL
    Xuechun Liu

    Beijing University of Technology, China

    1 publications · 20 citations

    1

    h-index

1–5 of 122

Venues

  1. Psychological Bulletin

    2 publications · 94 citations

    2

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 2 publications · 59 citations

    2

    h-index

  3. Multivariate Behavioral Research

    MBR · 2 publications · 34 citations

    2

    h-index

  4. Pattern Recognition Letters

    1 publications · 14 citations

    1

    h-index

  5. International Journal of Biometeorology

    1 publications · 60 citations

    1

    h-index

1–5