Training Set Size Optimization
Training Set Size Optimization is a methodological framework within training population design that systematically determines the minimal data sample size required to achieve desired model performance, distinguishing itself from related methods by focusing explicitly on the trade-off between dataset scale and learning efficiency.
176
Publications
13K
Citations
595
Authors
282
Institutions
Publications per year
2017–2026
75
Parents
176
CNN architectures for large-scale audio classification
Shawn Hershey, Sourish Chaudhuri, Daniel P. W. Ellis et al. · 2017 · 2.5K citations
1–5 of 176
595
4
h-index
4
h-index
3
h-index
2
h-index
2
h-index
1–5 of 595
282
Toronto Metropolitan University
Toronto, Canada
4 h-index
9 publications
1.1K citations
Google (United States)
Mountain View, United States
4 h-index
41 publications
32.5K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
3 h-index
9 publications
445 citations
Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement
Paris, France
3 h-index
11 publications
457 citations

Universidad de Granada
Granada, Spain
3 h-index
8 publications
542 citations
University of Bern
Bern, Switzerland
3 h-index
6 publications
146 citations
Technical University of Munich
Munich, Germany
3 h-index
7 publications
228 citations

Centre National de la Recherche Scientifique
Paris, France
3 h-index
8 publications
310 citations
1–8 of 282
Repository · 8 publications · 102 citations
8
h-index
4 publications · 177 citations
4
h-index
4 publications · 94 citations
4
h-index
4 publications · 295 citations
4
h-index
3 publications · 373 citations
3
h-index
1–5