Analysis of boosting algorithms using the smooth margin function

DOI10.1214/009053607000000785zbMath1132.68827arXiv0803.4092OpenAlexW2079484126MaRDI QIDQ2473080

Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies

Publication date: 26 February 2008

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

Full work available at URL: https://arxiv.org/abs/0803.4092

zbMATH Keywords

convergence ratesAdaBoostboostingcoordinate descentlarge margin classificationarc-gv

Mathematics Subject Classification ID

Computational learning theory (68Q32) Analysis of algorithms and problem complexity (68Q25) Analysis of algorithms (68W40)

Related Items (3)

A precise high-dimensional asymptotic theory for boosting and minimum-\(\ell_1\)-norm interpolated classifiers ⋮ On the equivalence of weak learnability and linear separability: new relaxations and efficient boosting algorithms ⋮ Random Coincidence Point Theorem in Fréchet Spaces with Applications

Uses Software

• AdaBoost.MH

Cites Work

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• Complexities of convex combinations and bounding the generalization error in classification
• Boosting with early stopping: convergence and consistency
• Boosting first-order learning
• Learning Theory
• Learning Theory
• Soft margins for AdaBoost
• Logistic regression, AdaBoost and Bregman distances

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