Support Vector Machines

Support Vector Machines is a supervised learning algorithm that belongs to the class of linear classifiers and is distinguished by its use of maximizing the margin between classes through optimal hyperplane separation in high-dimensional space.

42.8K

Publications

2.8M

Citations

102.9K

Authors

10.9K

Institutions

Publications per year

2017–2026

21.2K

Authors

102.9K

  1. JA
    Johan A. K. Suykens

    KU Leuven, Belgium

    116 publications · 11.6K citations

    53

    h-index

  2. BS
    Bernhard Schölkopf

    Max Planck Institute for Intelligent Systems, Germany

    65 publications · 51.5K citations

    46

    h-index

  3. CL
    Chih‐Jen Lin

    National Taiwan University, Taiwan

    51 publications · 65K citations

    38

    h-index

  4. JA
    Jón Atli Benediktsson

    University of Iceland, Iceland

    52 publications · 9.7K citations

    35

    h-index

  5. SS
    Shahaboddin Shamshirband

    University of Malaya, Malaysia

    42 publications · 4.1K citations

    33

    h-index

1–5 of 102.9K

Venues

  1. Expert Systems with Applications

    469 publications · 50.3K citations

    115

    h-index

  2. IEEE Access

    746 publications · 46.5K citations

    101

    h-index

  3. Neurocomputing

    449 publications · 50.9K citations

    89

    h-index

  4. IEEE Transactions on Geoscience and Remote Sensing

    170 publications · 31.3K citations

    87

    h-index

  5. Sensors

    523 publications · 29.4K citations

    84

    h-index

1–5