Minimum complexity regression estimation with weakly dependent observations
- 1 January 1996
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Information Theory
- Vol. 42 (6) , 2133-2145
- https://doi.org/10.1109/18.556602
Abstract
No abstract availableThis publication has 22 references indexed in Scilit:
- Concept learning using complexity regularizationIEEE Transactions on Information Theory, 1996
- Sup-norm approximation bounds for networks through probabilistic methodsIEEE Transactions on Information Theory, 1995
- Nonparametric estimation via empirical risk minimizationIEEE Transactions on Information Theory, 1995
- Degree of Approximation Results for Feedforward Networks Approximating Unknown Mappings and Their DerivativesNeural Computation, 1994
- Rates of Convergence for Empirical Processes of Stationary Mixing SequencesThe Annals of Probability, 1994
- Strong universal consistency of neural network classifiersIEEE Transactions on Information Theory, 1993
- Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappingsNeural Networks, 1990
- Conditions for linear processes to be strong-mixingProbability Theory and Related Fields, 1981
- Mixing Conditions for Markov ChainsTheory of Probability and Its Applications, 1974
- Probability Inequalities for Sums of Bounded Random VariablesJournal of the American Statistical Association, 1963