Characterizing \(L_{2}\)Boosting

DOI10.1214/12-AOS997zbMath1274.62420arXiv1207.5367MaRDI QIDQ447853

John Ehrlinger, Hemant Ishwaran

Publication date: 29 August 2012

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1207.5367

zbMATH Keywords

regularizationcritical directiongradient-correlationrepressibilitysolution path

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05) Linear inference, regression (62J99)

Related Items (3)

Toward Efficient Ensemble Learning with Structure Constraints: Convergent Algorithms and Applications ⋮ Characterizing \(L_{2}\)Boosting ⋮ Boosted nonparametric hazards with time-dependent covariates

Cites Work

• Greedy function approximation: A gradient boosting machine.
• Characterizing \(L_{2}\)Boosting
• Comment: Boosting algorithms: regularization, prediction and model fitting
• Least angle regression. (With discussion)
• Forward stagewise regression and the monotone lasso
• Boosting for high-dimensional linear models
• Boosting With theL₂Loss
• Matching pursuits with time-frequency dictionaries
• Regularization and Variable Selection Via the Elastic Net
• Unnamed Item

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