Ensemble Learning
Ensemble Learning is a machine learning paradigm that combines multiple base models within a statistical learning framework to improve predictive accuracy and robustness beyond what individual models can achieve.
18.9K
Publications
1.5M
Citations
53.8K
Authors
8.1K
Institutions
Publications per year
2017–2026
13.1K
18.9K
Greedy function approximation: A gradient boosting machine.
Jerome H. Friedman · The Annals of Statistics · 2001 · 29K citations · Full text
UCI Machine Learning Repository
Arthur Asuncion · Medical Entomology and Zoology · 2007 · 24.4K citations
Classification and Regression by randomForest
Andy Liaw, Matthew C. Wiener · 2007 · 18.4K citations
Distilling the Knowledge in a Neural Network
Geoffrey E. Hinton, Oriol Vinyals · arXiv (Cornell University) · 2015 · 14K citations · Full text
1–5 of 18.9K
53.8K
Nanyang Technological University, Singapore
50 publications · 5.6K citations
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34
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34
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32
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1–5 of 53.8K
8.1K
Nanyang Technological University
Singapore, Singapore
75 h-index
411 publications
88.7K citations

Chinese Academy of Sciences
Beijing, China
71 h-index
764 publications
45.1K citations

City University of Hong Kong
Hong Kong, Hong Kong
52 h-index
203 publications
20.5K citations

King Abdulaziz University
Jeddah, Saudi Arabia
51 h-index
198 publications
11.1K citations
Duy Tan University
Da Nang, Vietnam
50 h-index
74 publications
8.7K citations
Tsinghua University
Beijing, China
50 h-index
320 publications
26.6K citations

Centre National de la Recherche Scientifique
Paris, France
48 h-index
307 publications
64.8K citations

South China University of Technology
Guangzhou, China
48 h-index
370 publications
26.6K citations
1–8 of 8.1K
352 publications · 31.8K citations
87
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494 publications · 32.3K citations
82
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Repository · 512 publications · 55.7K citations
77
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328 publications · 33.1K citations
76
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208 publications · 21.5K citations
73
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