Mercer Kernels

Mercer Kernels is a type of positive semi-definite kernel function that satisfies Mercer's condition, ensuring it can be expressed as an inner product in a reproducing kernel Hilbert space, thereby guaranteeing the associated kernel matrix is symmetric and positive semi-definite for any set of input points.

112

Publications

10.7K

Citations

246

Authors

137

Institutions

Publications per year

2017–2026

9

Authors

246

  1. V‎
    V‎. ‎A‎. Menegatto

    Universidade Federal de São Carlos, Brazil

    5 publications · 91 citations

    5

    h-index

  2. DZ
    Ding‐Xuan Zhou

    City University of Hong Kong, Hong Kong SAR China

    4 publications · 367 citations

    4

    h-index

  3. NM
    Nasser M. Nasrabadi

    DEVCOM Army Research Laboratory, United States

    4 publications · 111 citations

    4

    h-index

  4. JC
    José C. Prı́ncipe

    University of Florida, United States

    4 publications · 915 citations

    4

    h-index

  5. FB
    Francis Bach

    Institut national de recherche en sciences et technologies du numérique, France

    4 publications · 204 citations

    4

    h-index

1–5 of 246

Venues

  1. IEEE Transactions on Signal Processing

    5 publications · 2K citations

    5

    h-index

  2. IEEE Transactions on Neural Networks

    5 publications · 1.6K citations

    5

    h-index

  3. Mathematische Nachrichten

    2 publications · 50 citations

    2

    h-index

  4. Journal of Complexity

    2 publications · 127 citations

    2

    h-index

  5. SIAM Journal on Mathematical Analysis

    2 publications · 65 citations

    2

    h-index

1–5