Kernel Methods
Kernel Methods is a class of algorithms in machine learning and statistical learning that employ positive definite kernels to implicitly map input data into higher-dimensional feature spaces, enabling the use of linear methods for nonlinear pattern analysis by leveraging the kernel trick.
22.8K
Publications
1.7M
Citations
46.5K
Authors
6.7K
Institutions
Publications per year
2017–2026
7.7K
Variants
Kernel-based AlgorithmsKernel-based Learning
22.8K
Chih-Chung Chang, Chih‐Jen Lin · ACM Transactions on Intelligent Systems and Technology · 2011 · 41.3K citations
An introduction to ROC analysis
Tom Fawcett · Pattern Recognition Letters · 2005 · 21.3K citations
A tutorial on support vector regression
Alex Smola, Bernhard Schölkopf · Statistics and Computing · 2004 · 13K citations
1–5 of 22.8K
46.5K
Max Planck Institute for Intelligent Systems, Germany
99 publications · 68.5K citations
60
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47
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38
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34
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33
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1–5 of 46.5K
6.7K

Chinese Academy of Sciences
Beijing, China
78 h-index
825 publications
48.8K citations

Centre National de la Recherche Scientifique
Paris, France
70 h-index
744 publications
47.7K citations
University of California, Berkeley
Berkeley, United States
69 h-index
330 publications
51.8K citations
Nanyang Technological University
Singapore, Singapore
69 h-index
384 publications
37.7K citations
Max Planck Institute for Biological Cybernetics
Tübingen, Germany
68 h-index
242 publications
84K citations
Max Planck Society
Munich, Germany
62 h-index
256 publications
65.3K citations

Harbin Institute of Technology
Harbin, China
61 h-index
503 publications
25.1K citations
Carnegie Mellon University
Pittsburgh, United States
60 h-index
386 publications
29.6K citations
1–8 of 6.7K
228 publications · 34.8K citations
99
h-index
TPAMI · 194 publications · 65.8K citations
97
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Repository · 638 publications · 37.1K citations
81
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274 publications · 29K citations
80
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261 publications · 22K citations
79
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1–5