Radial Basis Function Kernels

Radial Basis Function Kernels is a kernel function that belongs to the class of positive definite functions and is distinguished by its dependence on the Euclidean distance between input vectors, typically expressed as an exponentially decreasing function of the squared norm.

1.4K

Publications

68.8K

Citations

3.9K

Authors

1.4K

Institutions

Publications per year

2017–2026

555

Authors

3.9K

  1. EL
    Elisabeth Larsson

    Uppsala University, Sweden

    11 publications · 999 citations

    11

    h-index

  2. RS
    Robert Schaback

    University of Göttingen, Germany

    10 publications · 617 citations

    10

    h-index

  3. RB
    Ram Bilas Pachori

    Indian Institute of Technology Indore, India

    7 publications · 640 citations

    7

    h-index

  4. SC
    Sheng Chen

    University of Southampton, United Kingdom

    7 publications · 326 citations

    7

    h-index

  5. JA
    Johan A. K. Suykens

    KU Leuven, Belgium

    7 publications · 1.3K citations

    7

    h-index

1–5 of 3.9K

Venues

  1. Engineering Analysis with Boundary Elements

    39 publications · 1.7K citations

    25

    h-index

  2. Computers & Mathematics with Applications

    31 publications · 2.7K citations

    22

    h-index

  3. Journal of Computational and Applied Mathematics

    15 publications · 887 citations

    15

    h-index

  4. IEEE Transactions on Neural Networks

    14 publications · 2.7K citations

    14

    h-index

  5. Journal of Computational Physics

    15 publications · 933 citations

    14

    h-index

1–5