Boosting in the Presence of Outliers: Adaptive Classification With Nonconvex Loss Functions

DOI10.1080/01621459.2016.1273116zbMath1398.62167arXiv1510.01064OpenAlexW2191029871MaRDI QIDQ4962433

Alexander Hanbo Li, Jelena Bradic

Publication date: 2 November 2018

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1510.01064

zbMATH Keywords

classificationrobustnessinfluence functionbreakdown pointnonconvexityboosting

Mathematics Subject Classification ID

Nonparametric robustness (62G35) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items

AdaBoost Semiparametric Model Averaging Prediction for Multiple Categories ⋮ All-in-one robust estimator of the Gaussian mean ⋮ A precise high-dimensional asymptotic theory for boosting and minimum-\(\ell_1\)-norm interpolated classifiers ⋮ On the noise estimation statistics ⋮ Robust boosting with truncated loss functions ⋮ MM for penalized estimation ⋮ Robust boosting for regression problems ⋮ Unnamed Item

Uses Software

• ElemStatLearn
• RBoost

Cites Work

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