Hinge Loss
Hinge Loss is a loss function used in classification tasks that belongs to the family of margin-based loss functions, distinguished by its piecewise linear formulation which penalizes predictions based on their deviation from a margin boundary, specifically assigning zero loss to correct classifications that exceed the margin and linear loss otherwise.
325
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27.8K
Citations
944
Authors
392
Institutions
Publications per year
2017–2026
142
Parents
325
Learning Deep Representation for Imbalanced Classification
Chen Huang, Yining Li, Chen Change Loy et al. · 2016 · 1K citations
Minimal Loss Hashing for Compact Binary Codes
Mohammad Norouzi, David M. Blei · 2011 · 704 citations
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Chinese Academy of Sciences
Beijing, China
12 h-index
27 publications
1.3K citations
Nanyang Technological University
Singapore, Singapore
10 h-index
25 publications
1.5K citations
University of Chinese Academy of Sciences
Beijing, China
9 h-index
15 publications
391 citations

Centre National de la Recherche Scientifique
Paris, France
7 h-index
11 publications
228 citations
University of Michigan
Ann Arbor, United States
7 h-index
14 publications
3.6K citations
Carnegie Mellon University
Pittsburgh, United States
7 h-index
16 publications
1.6K citations
Toyota Technological Institute at Chicago
Chicago, United States
6 h-index
14 publications
595 citations
The University of Tokyo
Tokyo, Japan
6 h-index
12 publications
1.2K citations
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Repository · 29 publications · 869 citations
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9 publications · 299 citations
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6 publications · 166 citations
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6 publications · 118 citations
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Conference · 6 publications · 399 citations
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