Convolution Kernels

Convolution Kernels is a mathematical function or matrix in the context of kernel methods that enables local feature extraction and translation-equivariant transformations by applying weighted local operations across structured data such as images, time series, or spatial domains.

1.8K

Publications

148.6K

Citations

5.9K

Authors

1.5K

Institutions

Publications per year

2017–2026

1.2K

Authors

5.9K

  1. TC
    Taco Cohen

    University of Amsterdam, Netherlands

    11 publications · 812 citations

    11

    h-index

  2. MW
    Maurice Weiler

    University of Amsterdam, Netherlands

    11 publications · 820 citations

    10

    h-index

  3. MW
    Max Welling

    University of Amsterdam, Netherlands

    11 publications · 786 citations

    10

    h-index

  4. AM
    Alessandro Moschitti

    University of Trento, Italy

    8 publications · 633 citations

    8

    h-index

  5. LG
    Leonidas Guibas

    Stanford University, United States

    7 publications · 1.5K citations

    7

    h-index

1–5 of 5.9K

Venues

  1. arXiv (Cornell University)

    Repository · 166 publications · 20.3K citations

    54

    h-index

  2. IEEE Access

    50 publications · 2.5K citations

    27

    h-index

  3. 23

    h-index

  4. IEEE Transactions on Image Processing

    23 publications · 2.1K citations

    20

    h-index

  5. Sensors

    18 publications · 730 citations

    15

    h-index

1–5