Learning from uniformly ergodic Markov chains

DOI10.1016/j.jco.2009.01.001zbMath1183.68515OpenAlexW2001960189MaRDI QIDQ1023402

Hai Zhang, Bin Zou, Zong Ben Xu

Publication date: 11 June 2009

Published in: Journal of Complexity (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jco.2009.01.001

zbMATH Keywords

uniform convergencegeneralization boundrelative uniform convergenceERM algorithmsuniform ergodic Markov chain samples

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Markov processes (60J99)

Related Items (9)

Hold-out estimates of prediction models for Markov processes ⋮ Generalization bounds of ERM algorithm with Markov chain samples ⋮ Learning from regularized regression algorithms with \(p\)-order Markov chain sampling ⋮ Learning from non-irreducible Markov chains ⋮ Generalization performance of least-square regularized regression algorithm with Markov chain samples ⋮ Convergence and consistency of ERM algorithm with uniformly ergodic Markov chain samples ⋮ Generalized Dobrushin ergodicity coefficient and ergodicities of non-homogeneous Markov chains ⋮ Unnamed Item ⋮ Approximations of non-homogeneous Markov chains on abstract states spaces

Cites Work

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• Markov chains and stochastic stability
• Learning from dependent observations
• On the Markov chain central limit theorem
• Rates of convergence for empirical processes of stationary mixing sequences
• An introduction to MCMC for machine learning
• Hoeffding's inequality for uniformly ergodic Markov chains
• Best choices for regularization parameters in learning theory: on the bias-variance problem.
• New approaches to statistical learning theory
• The generalization performance of ERM algorithm with strongly mixing observations
• Mixing times for uniformly ergodic Markov chains
• The performance bounds of learning machines based on exponentially strongly mixing sequences
• On the mathematical foundations of learning
• Learning Theory
• Capacity of reproducing kernel spaces in learning theory
• ONLINE LEARNING WITH MARKOV SAMPLING
• Minimum complexity regression estimation with weakly dependent observations
• Shannon sampling and function reconstruction from point values
• Adaptive Rejection Sampling for Gibbs Sampling
• Stationarity detection in the initial transient problem
• Monte Carlo sampling methods using Markov chains and their applications

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