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• W2908274500 startingPage "2598" @default.
• W2908274500 abstract "Boosting is a learning scheme that combines weak learners to produce a strong composite learner, with the underlying intuition that one can obtain accurate learner by combining rough ones. This paper aims at developing a new boosting strategy, called rescaled boosting (RBoosting), to accelerate the numerical convergence rate and, consequently, improve learning performances of the original boosting. Our studies show that RBoosting possesses the almost optimal numerical convergence rate in the sense that, up to a logarithmic factor, it can reach the minimax nonlinear approximation rate. We then use RBoosting to tackle classification problems and deduce corresponding statistical consistency and tight generalization error estimates. A series of theoretical and experimental results shows that RBoosting outperforms boosting in terms of generalization." @default.
• W2908274500 created "2019-01-11" @default.
• W2908274500 creator A5022287632 @default.
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• W2908274500 date "2019-09-01" @default.
• W2908274500 modified "2023-10-17" @default.
• W2908274500 title "Rescaled Boosting in Classification" @default.
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• W2908274500 doi "https://doi.org/10.1109/tnnls.2018.2885085" @default.
• W2908274500 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30605106" @default.
• W2908274500 hasPublicationYear "2019" @default.
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