Kernel Methods

Kernel Methods is a class of algorithms in machine learning and statistical learning that employ positive definite kernels to implicitly map input data into higher-dimensional feature spaces, enabling the use of linear methods for nonlinear pattern analysis by leveraging the kernel trick.

22.8K

Publications

1.7M

Citations

46.5K

Authors

6.7K

Institutions

Publications per year

2017–2026

7.7K

Authors

46.5K

  1. BS
    Bernhard Schölkopf

    Max Planck Institute for Intelligent Systems, Germany

    99 publications · 68.5K citations

    60

    h-index

  2. JA
    Johan A. K. Suykens

    KU Leuven, Belgium

    123 publications · 8.4K citations

    47

    h-index

  3. JC
    José C. Prı́ncipe

    University of Florida, United States

    85 publications · 8.5K citations

    38

    h-index

  4. AG
    Arthur Gretton

    University College London, United Kingdom

    62 publications · 9.9K citations

    34

    h-index

  5. AS
    Alex Smola

    Carnegie Mellon University, United States

    38 publications · 32.5K citations

    33

    h-index

1–5 of 46.5K

Venues

  1. The Annals of Statistics

    228 publications · 34.8K citations

    99

    h-index

  2. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 194 publications · 65.8K citations

    97

    h-index

  3. arXiv (Cornell University)

    Repository · 638 publications · 37.1K citations

    81

    h-index

  4. Pattern Recognition

    274 publications · 29K citations

    80

    h-index

  5. Expert Systems with Applications

    261 publications · 22K citations

    79

    h-index

1–5