Learning Kernels

Learning Kernels is a methodological framework in machine learning that, as a subfield of kernel methods, specifically involves the systematic selection or construction of kernel functions from data-driven principles rather than relying on predefined or fixed kernels.

476

Publications

46.9K

Citations

1.4K

Authors

567

Institutions

Publications per year

2017–2026

161

Authors

1.4K

  1. BS
    Bernhard Schölkopf

    Max Planck Institute for Intelligent Systems, Germany

    10 publications · 3.9K citations

    10

    h-index

  2. MM
    Mehryar Mohri

    Google (United States), United States

    9 publications · 1.5K citations

    9

    h-index

  3. CC
    Corinna Cortes

    Google (United States), United States

    9 publications · 1.5K citations

    9

    h-index

  4. JW
    Jason Weston

    Meta (Israel), Israel

    7 publications · 3.4K citations

    7

    h-index

  5. AR
    Afshin Rostamizadeh

    Google (United States), United States

    6 publications · 1.3K citations

    6

    h-index

1–5 of 1.4K

Venues

  1. arXiv (Cornell University)

    Repository · 22 publications · 2K citations

    16

    h-index

  2. Neurocomputing

    22 publications · 1.8K citations

    16

    h-index

  3. Expert Systems with Applications

    16 publications · 933 citations

    15

    h-index

  4. Pattern Recognition

    17 publications · 676 citations

    14

    h-index

  5. Pattern Recognition Letters

    11 publications · 362 citations

    10

    h-index

1–5