Structured Kernel Learning
Structured Kernel Learning is a machine learning framework that extends kernel methods by incorporating explicit structural assumptions about the data space—such as hierarchies, graphs, or compositional rules—into the kernel design to enable more effective and interpretable feature representations.
14
Publications
616
Citations
40
Authors
20
Institutions
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2017–2026
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Parents
14
Kernelized structural SVM learning for supervised object segmentation
Luca Bertelli, Tian–Li Yu, Diem Vu et al. · 2011 · 103 citations
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University of Massachusetts Amherst
Amherst Center, United States
2 h-index
4 publications
114 citations

Clausthal University of Technology
Clausthal-Zellerfeld, Germany
2 h-index
3 publications
35 citations
Google (United States)
Mountain View, United States
2 h-index
6 publications
456 citations

Amherst College
Amherst Center, United States
2 h-index
4 publications
114 citations
Jiangxi University of Finance and Economics
Nanchang, China
1 h-index
2 publications
90 citations
National University of Singapore
Singapore, Singapore
1 h-index
3 publications
87 citations
University of North Carolina Health Care
Chapel Hill, United States
1 h-index
5 publications
220 citations
Institute of High Performance Computing
Singapore, Singapore
1 h-index
1 publications
29 citations
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Repository · 3 publications · 250 citations
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2 publications · 50 citations
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1 publications · 13 citations
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1 publications · 29 citations
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1 publications · 45 citations
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