Multi-boosting
Multi-Boosting is an ensemble learning method that extends boosting by combining multiple base learners through multi-dimensional optimization to enhance generalization performance while maintaining robustness against overfitting.
12
Publications
1K
Citations
45
Authors
39
Institutions
Publications per year
2017–2026
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Nanjing University of Posts and Telecommunications
Nanjing, China
1 h-index
2 publications
52 citations

Central South University
Changsha, China
1 h-index
1 publications
26 citations

Centre National de la Recherche Scientifique
Paris, France
1 h-index
1 publications
75 citations

Data61
Sydney, Australia
1 h-index
1 publications
22 citations
Universidade Tecnológica Federal do Paraná
Curitiba, Brazil
1 h-index
1 publications
520 citations
Stanford University
Stanford, United States
1 h-index
3 publications
558 citations

University of Tehran
Tehran, Iran
1 h-index
1 publications
58 citations
Pontifícia Universidade Católica do Paraná
Curitiba, Brazil
1 h-index
2 publications
1K citations
1–8 of 39
1 publications · 75 citations
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1 publications · 520 citations
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1 publications · 44 citations
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1 publications · 58 citations
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1 publications · 22 citations
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