A stochastic approximation view of boosting

DOI10.1016/j.csda.2007.06.020zbMath1452.62134OpenAlexW2041447205MaRDI QIDQ1020818

Yuan-chin Ivan Chang, Chen-Hai Andy Tsao

Publication date: 2 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2007.06.020

zbMATH Keywords

stochastic approximationboostingRobbins-Monro proceduresmooth early stopping

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Stochastic approximation (62L20)

Related Items (6)

An empirical bias–variance analysis of DECORATE ensemble method at different training sample sizes ⋮ Early stopping in \(L_{2}\)Boosting ⋮ Using boosting to prune double-bagging ensembles ⋮ Taxonomy for characterizing ensemble methods in classification tasks: a review and annotated bibliography ⋮ Editorial: Machine learning and robust data mining ⋮ SABoost

Uses Software

• UCI-ml
• UsingR
• AdaBoost.MH

Cites Work

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