Hard Margin Svm

Hard Margin SVM is a support vector machine formulation that belongs to the class of linear classifiers and is distinguished by its strict requirement that all training instances be correctly classified without allowing any margin violations, thereby seeking maximum margin separation only under the assumption of linearly separable data.

94

Publications

5.7K

Citations

337

Authors

158

Institutions

Publications per year

2017–2026

38

Authors

337

  1. MA
    Milad Arabloo

    Islamic Azad University North Tehran Branch, Iran

    2 publications · 90 citations

    2

    h-index

  2. KL
    Kenneth L. Clarkson

    AT&T (United States), United States

    2 publications · 118 citations

    2

    h-index

  3. DP
    David P. Woodruff

    IBM Research - Almaden, United States

    2 publications · 118 citations

    2

    h-index

  4. EH
    Elad Hazan

    Princeton University, United States

    2 publications · 118 citations

    2

    h-index

  5. WZ
    Wx Zhang

    Centre National de la Recherche Scientifique, France

    1 publications · 14 citations

    1

    h-index

1–5 of 337

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 118 citations

    4

    h-index

  2. Expert Systems with Applications

    3 publications · 137 citations

    3

    h-index

  3. IEEE Transactions on Neural Networks

    3 publications · 67 citations

    3

    h-index

  4. Neurocomputing

    2 publications · 85 citations

    2

    h-index

  5. Optik

    2 publications · 40 citations

    2

    h-index

1–5