Robust boosting with truncated loss functions

DOI10.1214/18-EJS1404zbMath1386.68143OpenAlexW2791518061MaRDI QIDQ82723

Zhu Wang, Zhu Wang

Publication date: 1 January 2018

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1519700496

zbMATH Keywords

machine learningboostingMM algorithmdifference of convexrobust method

Mathematics Subject Classification ID

Nonparametric robustness (62G35) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonconvex programming, global optimization (90C26) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

MM for penalized estimation ⋮ bst

Uses Software

• AdaBoost.MH

Cites Work

• Greedy function approximation: A gradient boosting machine.
• Boosting algorithms: regularization, prediction and model fitting
• Estimating the dimension of a model
• Convex analysis approach to d. c. programming: Theory, algorithms and applications
• Introductory lectures on convex optimization. A basic course.
• A note on margin-based loss functions in classification
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Robust penalized logistic regression with truncated loss functions
• Robust Truncated Hinge Loss Support Vector Machines
• Boosting in the Presence of Outliers: Adaptive Classification With Nonconvex Loss Functions
• All of Nonparametric Statistics
• Variable Selection for Support Vector Machines in Moderately High Dimensions
• Optimization
• An adaptive version of the boost by majority algorithm
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