Heading Severity

Heading Severity is a methodological construct within pruning techniques in machine learning that quantifies the extent of structural reduction applied to a model's architecture by measuring the depth and breadth of node or layer removal relative to its original topology.

4

Publications

87

Citations

24

Authors

7

Institutions

Publications per year

2017–2026

0

Authors

24

  1. PS
    Parisa Soleimani

    1 publications · 10 citations

    1

    h-index

  2. KN
    Kouji Nakamichi

    Western Region Agricultural Research Center, Japan

    1 publications · 10 citations

    1

    h-index

  3. LM
    Leslie Mann

    Agriculture and Agri-Food Canada, Canada

    1 publications · 39 citations

    1

    h-index

  4. YT
    Yasuaki Tamura

    Sapporo Medical University, Japan

    1 publications · 10 citations

    1

    h-index

  5. YG
    Yu Ge

    Chinese Academy of Tropical Agricultural Sciences, China

    1 publications · 28 citations

    1

    h-index

1–5 of 24

Venues

  1. Horticulture Environment and Biotechnology

    1 publications · 28 citations

    1

    h-index

  2. The Canadian Entomologist

    1 publications · 39 citations

    1

    h-index

  3. Breeding Science

    1 publications · 10 citations

    1

    h-index

  4. African Journal of Agricultural Research

    AJAR · 1 publications · 10 citations

    1

    h-index

1–4