Kernel Function Selection
Kernel Function Selection is the process of choosing a specific kernel function within the class of non-parametric density estimation methods that best balances bias and variance in the estimated probability density function according to the data structure and underlying assumptions.
184
Publications
8K
Citations
567
Authors
267
Institutions
Publications per year
2017–2026
67
Parents
184
Localized multiple kernel learning
Mehmet Gönen, Ethem Alpaydın · 2008 · 284 citations
General Parameterized Time-Frequency Transform
Yixin Yang, Zhike Peng, Xingjian Dong et al. · IEEE Transactions on Signal Processing · 2014 · 213 citations
Engineering, Biomedical Engineering, Biomedical Signal Processing +11
Scaling-Basis Chirplet Transform
Miaofen Li, Tianyang Wang, Fulei Chu et al. · IEEE Transactions on Industrial Electronics · 2020 · 208 citations
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267

National Yang Ming Chiao Tung University
Hsinchu, Taiwan
5 h-index
12 publications
643 citations

Northeastern University
Shenyang, China
4 h-index
12 publications
450 citations

Chinese Academy of Sciences
Beijing, China
4 h-index
7 publications
156 citations
Wuhan University
Wuhan, China
4 h-index
7 publications
647 citations
University of Chinese Academy of Sciences
Beijing, China
4 h-index
6 publications
149 citations
Tsinghua University
Beijing, China
4 h-index
13 publications
1.4K citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
6 publications
92 citations

Xi'an Jiaotong University
Xi'an, China
3 h-index
9 publications
415 citations
1–8 of 267
Repository · 5 publications · 95 citations
5
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3 publications · 149 citations
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AMR · 2 publications · 26 citations
2
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2 publications · 35 citations
2
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2 publications · 73 citations
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