Learning Kernels
Learning Kernels is a methodological framework in machine learning that, as a subfield of kernel methods, specifically involves the systematic selection or construction of kernel functions from data-driven principles rather than relying on predefined or fixed kernels.
476
Publications
46.9K
Citations
1.4K
Authors
567
Institutions
Publications per year
2017–2026
161
Parents
476
Fisher discriminant analysis with kernels
Gunnar Rätsch, Jason Weston, Bernhard Schölkopf et al. · 2003 · 2.7K citations
Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
Yingying Zhang, Desen Zhou, Siqin Chen et al. · 2016 · 2.3K citations
Machine Learning, Neural and Statistical Classification
Bill Fulkerson, D. Michie, D. J. Spiegelhalter et al. · Technometrics · 1995 · 2.2K citations
1–5 of 476
1.4K
Max Planck Institute for Intelligent Systems, Germany
10 publications · 3.9K citations
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567
Max Planck Society
Munich, Germany
13 h-index
38 publications
3.6K citations
Max Planck Institute for Biological Cybernetics
Tübingen, Germany
11 h-index
26 publications
2.6K citations
Google (United States)
Mountain View, United States
10 h-index
19 publications
3.3K citations

Chinese Academy of Sciences
Beijing, China
8 h-index
22 publications
1.2K citations
University College London
London, United Kingdom
8 h-index
16 publications
1K citations
Wenzhou University
Wenzhou, China
8 h-index
27 publications
3.8K citations

Harbin Institute of Technology
Harbin, China
8 h-index
15 publications
387 citations
Nanyang Technological University
Singapore, Singapore
7 h-index
16 publications
1.5K citations
1–8 of 567
Repository · 22 publications · 2K citations
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22 publications · 1.8K citations
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16 publications · 933 citations
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17 publications · 676 citations
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11 publications · 362 citations
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