Structured Kernel Learning

Structured Kernel Learning is a machine learning framework that extends kernel methods by incorporating explicit structural assumptions about the data space—such as hierarchies, graphs, or compositional rules—into the kernel design to enable more effective and interpretable feature representations.

14

Publications

616

Citations

40

Authors

20

Institutions

Publications per year

2017–2026

4

Authors

40

  1. SC
    Steven Cheng-Xian Li

    University of Massachusetts Amherst, United States

    2 publications · 57 citations

    2

    h-index

  2. BM
    Benjamin M. Marlin

    University of Massachusetts Amherst, United States

    2 publications · 57 citations

    2

    h-index

  3. HL
    Hongwei Liu

    Xidian University, China

    1 publications · 22 citations

    1

    h-index

  4. AS
    Alessandro Sperduti

    University of Padua, Italy

    1 publications · 13 citations

    1

    h-index

  5. CC
    Chee‐Kong Chui

    National University of Singapore, Singapore

    1 publications · 29 citations

    1

    h-index

1–5 of 40

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 250 citations

    3

    h-index

  2. IEEE Transactions on Image Processing

    2 publications · 50 citations

    2

    h-index

  3. Neural Networks

    1 publications · 13 citations

    1

    h-index

  4. IEEE Systems Man and Cybernetics Magazine

    1 publications · 29 citations

    1

    h-index

  5. Energy Conversion and Management

    1 publications · 45 citations

    1

    h-index

1–5