Extension of the PAC framework to finite and countable Markov chains

DOI10.1109/TIT.2002.806131zbMath1063.68060MaRDI QIDQ4679911

David Gamarnik

Publication date: 31 May 2005

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Mathematics Subject Classification ID

Computational learning theory (68Q32) Learning and adaptive systems in artificial intelligence (68T05) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10)

Related Items (7)

Generalization bounds of ERM algorithm with Markov chain samples ⋮ Robustness and generalization ⋮ Learning from non-irreducible Markov chains ⋮ Generalization bounds of ERM algorithm with \(V\)-geometrically ergodic Markov chains ⋮ On biased random walks, corrupted intervals, and learning under adversarial design ⋮ Generalization performance of least-square regularized regression algorithm with Markov chain samples ⋮ Mixing time estimation in reversible Markov chains from a single sample path

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