An oracle inequality for regularized risk minimizers with strongly mixing observations

DOI10.1007/s11464-013-0247-4zbMath1296.62138OpenAlexW2002831319MaRDI QIDQ373424

Xing Xing, Feilong Cao

Publication date: 22 October 2013

Published in: Frontiers of Mathematics in China (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11464-013-0247-4

zbMATH Keywords

oracle inequalityexponentially strongly mixingregularized risk minimizer

Mathematics Subject Classification ID

Computational learning theory (68Q32) Inequalities; stochastic orderings (60E15) General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Learning from non-identical sampling for classification
• Learning from dependent observations
• Fast rates for support vector machines using Gaussian kernels
• Rates of convergence for empirical processes of stationary mixing sequences
• Nonparametric time series prediction through adaptive model selection
• Rates of uniform convergence of empirical means with mixing processes
• New approaches to statistical learning theory
• On the mathematical foundations of learning
• INDEFINITE KERNEL NETWORK WITH DEPENDENT SAMPLING
• ONLINE REGRESSION WITH VARYING GAUSSIANS AND NON-IDENTICAL DISTRIBUTIONS
• A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
• Learning Theory
• Capacity of reproducing kernel spaces in learning theory
• ONLINE LEARNING WITH MARKOV SAMPLING
• Minimum complexity regression estimation with weakly dependent observations
• SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming
• On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
• Theory of Reproducing Kernels

This page was built for publication: An oracle inequality for regularized risk minimizers with strongly mixing observations