Squared Hinge Loss

Squared Hinge Loss is a loss function in machine learning that belongs to the class of margin-based loss functions, distinguished by its formulation as the squared positive part of the difference between one and the product of the true label and the predicted score, thereby penalizing misclassifications more severely than the standard hinge loss.

36

Publications

1.5K

Citations

105

Authors

72

Institutions

Publications per year

2017–2026

20

Authors

105

  1. IW
    Ivor W. Tsang

    University of Technology Sydney, Australia

    3 publications · 101 citations

    3

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  2. XW
    Xiaoqian Wang

    The University of Texas at Arlington, United States

    2 publications · 80 citations

    2

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  3. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    2 publications · 80 citations

    2

    h-index

  4. SC
    Steven C. H. Hoi

    Nanyang Technological University, Singapore

    2 publications · 91 citations

    2

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  5. JZ
    Jinfeng Zhuang

    Nanyang Technological University, Singapore

    2 publications · 91 citations

    2

    h-index

1–5 of 105

Venues

  1. Pattern Recognition

    3 publications · 204 citations

    3

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  2. arXiv (Cornell University)

    Repository · 3 publications · 58 citations

    3

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  3. Neural Networks

    3 publications · 76 citations

    3

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  4. 2

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  5. Neural Computation

    2 publications · 124 citations

    2

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1–5