PAC learning in non-linear FIR models

DOI<link itemprop=identifier href="https://doi.org/10.1002/1099-1115(200102)15:1<37::AID-ACS626>3.0.CO;2-7" /><37::AID-ACS626>3.0.CO;2-7 10.1002/1099-1115(200102)15:1<37::AID-ACS626>3.0.CO;2-7zbMath0973.93063OpenAlexW2059975644MaRDI QIDQ2708066

Kayvan Najarian, Guy A. Dumont, Nancy E. Heckman, Michael S. Davies

Publication date: 13 July 2001

Full work available at URL: https://doi.org/10.1002/1099-1115(200102)15:1<37::aid-acs626>3.0.co;2-7

zbMATH Keywords

learningPAC learningprobabilistic methodsnonlinear finite impulse response modelsprobably approximately correct learning theory

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Stochastic learning and adaptive control (93E35)

Related Items (2)

An automated optimal engagement and attention detection system using electrocardiogram ⋮ A fixed-distribution PAC learning theory for neural FIR models

Cites Work

• Unnamed Item
• A theory of the learnable
• Recursive hybrid algorithm for non-linear system identification using radial basis function networks
• Minimum complexity regression estimation with weakly dependent observations
• On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities

This page was built for publication: PAC learning in non-linear FIR models