Sampling Strategy Optimization
Sampling Strategy Optimization is a methodological framework that systematically determines the most efficient and representative approach to selecting samples within a given population or process, distinguishing itself by integrating statistical rigor, resource constraints, and measurement objectives to enhance decision accuracy in contexts such as Continued Process Verification and Grape Maturity Assessment.
305
Publications
11.6K
Citations
1.1K
Authors
475
Institutions
Publications per year
2017–2026
100
305
Sample size selection in optimization methods for machine learning
Richard H. Byrd, Gillian M. Chin, Jorge Nocedal et al. · Mathematical Programming · 2012 · 395 citations
Action Localization with Tubelets from Motion
Mihir Jain, Jan van Gemert, Hervé Jeǵou et al. · 2014 · 262 citations · Full text
1–5 of 305
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Pohang University of Science and Technology, South Korea
10 publications · 384 citations
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King Abdulaziz University
Jeddah, Saudi Arabia
9 h-index
9 publications
135 citations
Pohang University of Science and Technology
Pohang, South Korea
9 h-index
9 publications
351 citations
Kalasalingam Academy of Research and Education
Srivilliputhur, India
7 h-index
11 publications
229 citations

Shanghai Jiao Tong University
Shanghai, China
6 h-index
16 publications
251 citations

Chinese Academy of Sciences
Beijing, China
6 h-index
18 publications
447 citations

Xidian University
Xi'an, China
6 h-index
13 publications
268 citations
University of Veterinary and Animal Sciences
Lahore, Pakistan
6 h-index
7 publications
98 citations
Forman Christian College
Lahore, Pakistan
5 h-index
8 publications
506 citations
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MRM · 6 publications · 421 citations
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5 publications · 163 citations
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5 publications · 154 citations
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4 publications · 64 citations
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4 publications · 160 citations
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