Improving nonparametric regression methods by bagging and boosting.

DOI10.1016/S0167-9473(01)00068-8zbMath1072.62562MaRDI QIDQ5958473

Agostino Di Ciaccio, Simone Borra

Publication date: 3 March 2002

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

zbMATH Keywords

Nonparametric regressionBoostingBaggingDARTMARSPPR

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08)

Related Items (7)

Boosting for real and functional samples: an application to an environmental problem ⋮ Boosting of granular models ⋮ Degrees of freedom and model selection in semiparametric additive monotone regression ⋮ Cross-validated bagged learning ⋮ Boosting and instability for regression trees ⋮ Ensemble classification based on generalized additive models ⋮ ESTIMATING A PARAMETER WHEN IT IS KNOWN THAT THE PARAMETER EXCEEDS A GIVEN VALUE

Uses Software

• AdaBoost.MH

Cites Work

• Greedy function approximation: A gradient boosting machine.
• Bagging predictors
• On bagging and nonlinear estimation
• Multivariate adaptive regression splines
• A decision-theoretic generalization of on-line learning and an application to boosting
• A geometric approach to leveraging weak learners
• Performance evaluation of bagging and boosting in nonparametric regression.
• Arcing classifiers. (With discussion)
• Boosting the margin: a new explanation for the effectiveness of voting methods
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Using iterated bagging to debias regressions

This page was built for publication: Improving nonparametric regression methods by bagging and boosting.