Support Vector Regression
Support Vector Regression is a regression method that extends the principles of Support Vector Machines by employing structural risk minimization and kernel functions to predict continuous outcomes through the identification of a function that deviates from the observed data by no more than a specified margin, while maximizing the generalizability of the model.
8.7K
Publications
479.9K
Citations
29.5K
Authors
5.6K
Institutions
Publications per year
2017–2026
5.2K
8.7K
A tutorial on support vector regression
Alex Smola, Bernhard Schölkopf · Statistics and Computing · 2004 · 13K citations
Support Vector Regression Machines
Harris Drucker, Christopher J. C. Burges, Linda Kaufman et al. · 1996 · 4.2K citations
Support Vector Machines for Classification and Regression
S.R. Gunn · 1998 · 2.8K citations
Bernhard Schölkopf, Alex Smola, Robert C. Williamson et al. · Neural Computation · 2000 · 2.8K citations
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Asia Eastern University of Science and Technology, Taiwan
24 publications · 2.6K citations
21
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17
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17
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5.6K

Chinese Academy of Sciences
Beijing, China
56 h-index
620 publications
35K citations

University of Tehran
Tehran, Iran
44 h-index
180 publications
10.4K citations
Nanyang Technological University
Singapore, Singapore
41 h-index
184 publications
15.4K citations

Shanghai Jiao Tong University
Shanghai, China
40 h-index
278 publications
14.3K citations
Duy Tan University
Da Nang, Vietnam
39 h-index
66 publications
4.9K citations

Zhejiang University
Hangzhou, China
38 h-index
320 publications
11.9K citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
37 h-index
103 publications
7.4K citations

Huazhong University of Science and Technology
Wuhan, China
36 h-index
273 publications
24.4K citations
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107 publications · 10.8K citations
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151 publications · 7.5K citations
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133 publications · 7.1K citations
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122 publications · 6.2K citations
46
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76 publications · 7K citations
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