Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings
- 1 January 1990
- journal article
- Published by Elsevier in Neural Networks
- Vol. 3 (5) , 535-549
- https://doi.org/10.1016/0893-6080(90)90004-5
Abstract
No abstract availableKeywords
This publication has 14 references indexed in Scilit:
- Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networksPublished by Elsevier ,2003
- A new approach for finding the global minimum of error function of neural networksNeural Networks, 1989
- Approximation by superpositions of a sigmoidal functionMathematics of Control, Signals, and Systems, 1989
- On the approximate realization of continuous mappings by neural networksNeural Networks, 1989
- Multilayer feedforward networks are universal approximatorsNeural Networks, 1989
- Asymptotic Optimality for $C_p, C_L$, Cross-Validation and Generalized Cross-Validation: Discrete Index SetThe Annals of Statistics, 1987
- Semi-Nonparametric Maximum Likelihood EstimationEconometrica, 1987
- Multivariate Smoothing Spline FunctionsSIAM Journal on Numerical Analysis, 1984
- Nonparametric Maximum Likelihood Estimation by the Method of SievesThe Annals of Statistics, 1982
- 𝜀-entropy and 𝜀-capacity of sets in functional spacesPublished by American Mathematical Society (AMS) ,1961