Ensemble Sampling is a methodological framework in machine learning that belongs to the class of ensemble techniques by constructing multiple predictive models through resampling strategies applied to the training data, distinguished by its focus on generating diverse base learners via systematic sampling variations such as bootstrapping or subsampling.
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Dong Xibin, Zhiwen Yu, Wenming Cao et al. · Frontiers of Computer Science · 2019 · 1.9K citations
Computing, Data Science, Data Mining +11
MONTE CARLO METHODS IN GEOPHYSICAL INVERSE PROBLEMS
Malcolm Sambridge, Klaus Mosegaard · Reviews of Geophysics · 2002 · 826 citations · Full text
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442
Florida Atlantic University
Boca Raton, United States
9 h-index
20 publications
4.2K citations
National University of Defense Technology
Changsha, China
6 h-index
21 publications
1.3K citations
Stanford University
Stanford, United States
6 h-index
14 publications
483 citations
Universidad de Granada
Granada, Spain
6 h-index
13 publications
4.1K citations
Nanjing University
Nanjing, China
5 h-index
15 publications
348 citations
University of Pittsburgh
Pittsburgh, United States
4 h-index
10 publications
1.1K citations
University of California, Berkeley
Berkeley, United States
4 h-index
7 publications
465 citations
Seoul National University
Seoul, South Korea
4 h-index
6 publications
286 citations
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13 publications · 2K citations
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- arXiv (Cornell University)
Repository · 14 publications · 3.4K citations
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- Neurocomputing
8 publications · 635 citations
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