Mixture Of Classifiers
Mixture of Classifiers is a classifier ensemble method that combines multiple base classifiers through weighted aggregation of their outputs, where the weights are typically learned from data to optimize classification performance.
517
Publications
54.4K
Citations
1.4K
Authors
600
Institutions
Publications per year
2017–2026
142
Parents
517
Statistical Comparisons of Classifiers over Multiple Data Sets
Janez Demšar · 2006 · 11.2K citations
Josef Kittler, M. Hatef, Robert P. W. Duin et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 1998 · 5.3K citations
Combining Pattern Classifiers: Methods and Algorithms
Subhash Bagui · Technometrics · 2005 · 3.2K citations
Ensemble based systems in decision making
Robi Polikar · IEEE Circuits and Systems Magazine · 2006 · 2.9K citations
1–5 of 517
1.4K
8
h-index
5
h-index
5
h-index
4
h-index
4
h-index
1–5 of 1.4K
600

Concordia University
Montreal, Canada
9 h-index
21 publications
8.8K citations

University of Surrey
Guildford, United Kingdom
6 h-index
12 publications
11.1K citations
National University of Singapore
Singapore, Singapore
6 h-index
9 publications
786 citations
Pontifícia Universidade Católica do Paraná
Curitiba, Brazil
6 h-index
11 publications
305 citations
Carnegie Mellon University
Pittsburgh, United States
6 h-index
15 publications
433 citations

Chinese Academy of Sciences
Beijing, China
6 h-index
11 publications
285 citations
University of South Florida
Tampa, United States
5 h-index
11 publications
2.3K citations
Nanjing University
Nanjing, China
5 h-index
12 publications
466 citations
1–8 of 600
11 publications · 430 citations
11
h-index
Repository · 10 publications · 216 citations
10
h-index
10 publications · 956 citations
10
h-index
8 publications · 584 citations
8
h-index
TPAMI · 8 publications · 7.1K citations
8
h-index
1–5