Classification with Gaussians and convex loss. II: Improving error bounds by noise conditions

DOI10.1007/s11425-010-4043-2zbMath1215.68203OpenAlexW1993410613MaRDI QIDQ547325

Dao-Hong Xiang

Publication date: 1 July 2011

Published in: Science China. Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11425-010-4043-2

zbMATH Keywords

Sobolev spacereproducing kernel Hilbert spacebinary classificationgeneral convex lossTsybakov noise condition

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (6)

Learning rates of kernel-based robust classification ⋮ The convergence rates of Shannon sampling learning algorithms ⋮ Fully corrective gradient boosting with squared hinge: fast learning rates and early stopping ⋮ Quantitative convergence analysis of kernel based large-margin unified machines ⋮ Learning Rates for Classification with Gaussian Kernels ⋮ Comparison theorems on large-margin learning

Cites Work

• Multi-kernel regularized classifiers
• Fast rates for support vector machines using Gaussian kernels
• Statistical behavior and consistency of classification methods based on convex risk minimization.
• Optimal aggregation of classifiers in statistical learning.
• Learning Theory
• SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming
• Convexity, Classification, and Risk Bounds
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Classification with Gaussians and convex loss. II: Improving error bounds by noise conditions