Complexity regularization via localized random penalties
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Publication:1879970
DOI10.1214/009053604000000463zbMath1045.62060arXivmath/0410091OpenAlexW3098965260MaRDI QIDQ1879970
Gábor Lugosi, Marten H. Wegkamp
Publication date: 15 September 2004
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/math/0410091
classificationconcentration inequalitiesoracle inequalitiescomplexity regularizationRademacher averagesrandom penaltiesshatter coefficients
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Inequalities; stochastic orderings (60E15) Nonparametric inference (62G99)
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Cites Work
- Some limit theorems for empirical processes (with discussion)
- A result of Vapnik with applications
- Concentration inequalities using the entropy method
- Adaptive model selection using empirical complexities
- A Bennett concentration inequality and its application to suprema of empirical processes
- Empirical margin distributions and bounding the generalization error of combined classifiers
- About the constants in Talagrand's concentration inequalities for empirical processes.
- New approaches to statistical learning theory
- Optimal aggregation of classifiers in statistical learning.
- Weak convergence and empirical processes. With applications to statistics
- A new look at independence
- On Talagrand's deviation inequalities for product measures
- Theorie der Zeichenerkennung
- A sharp concentration inequality with applications
- Rademacher penalties and structural risk minimization
- Structural risk minimization over data-dependent hierarchies
- Some applications of concentration inequalities to statistics
- Concentration inequalities for set-indexed empirical processes
- Model selection and error estimation
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