Transfer Function Learning

Transfer Function Learning is a computational methodology that, as a specialized form of transfer learning, focuses on the systematic acquisition and adaptation of functional mappings from source domains to target domains to improve model performance under domain shift.

91

Publications

4.9K

Citations

347

Authors

169

Institutions

Publications per year

2017–2026

69

Authors

347

  1. WD
    Włodzisław Duch

    Nicolaus Copernicus University, Poland

    3 publications · 126 citations

    3

    h-index

  2. NJ
    Norbert Jankowski

    Nicolaus Copernicus University, Poland

    3 publications · 126 citations

    3

    h-index

  3. XM
    Xiaojuan Ma

    Hong Kong University of Science and Technology, Hong Kong SAR China

    2 publications · 171 citations

    2

    h-index

  4. QY
    Qiang Yang

    Hong Kong University of Science and Technology, Hong Kong SAR China

    2 publications · 171 citations

    2

    h-index

  5. GX
    Geng Xu

    Hong Kong University of Science and Technology, Hong Kong SAR China

    2 publications · 171 citations

    2

    h-index

1–5 of 347

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 482 citations

    4

    h-index

  2. 3

    h-index

  3. Knowledge-Based Systems

    3 publications · 187 citations

    3

    h-index

  4. Neurocomputing

    3 publications · 108 citations

    3

    h-index

  5. IEEE Transactions on Neural Networks

    3 publications · 112 citations

    3

    h-index

1–5