Maximum Margin Classification

Maximum Margin Classification is a classification method that belongs to the family of linear discriminant models and is distinguished by its objective of maximizing the geometric margin between the separating hyperplane and the nearest data points of each class to enhance generalization performance.

377

Publications

41.5K

Citations

908

Authors

397

Institutions

Publications per year

2017–2026

76

Authors

908

  1. JS
    John Shawe‐Taylor

    University College London, United Kingdom

    11 publications · 2.6K citations

    10

    h-index

  2. RE
    Robert E. Schapire

    AT&T (United States), United States

    9 publications · 5K citations

    9

    h-index

  3. PL
    Peter L. Bartlett

    University of California, Berkeley, United States

    6 publications · 3.1K citations

    6

    h-index

  4. YX
    Yitian Xu

    China Agricultural University, China

    5 publications · 222 citations

    5

    h-index

  5. LJ
    Licheng Jiao

    Xidian University, China

    4 publications · 117 citations

    4

    h-index

1–5 of 908

Venues

  1. Pattern Recognition

    22 publications · 688 citations

    16

    h-index

  2. Neurocomputing

    17 publications · 433 citations

    14

    h-index

  3. arXiv (Cornell University)

    Repository · 16 publications · 599 citations

    13

    h-index

  4. 12

    h-index

  5. IEEE Transactions on Neural Networks

    13 publications · 901 citations

    11

    h-index

1–5