The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions
Abstract
K.M. Hornik, M. Stinchcombe, and H. White (Univ. of California at San Diego, Dept. of Economics Discussion Paper, June 1988; to appear in Neural Networks) showed that multilayer feedforward networks with as few as one hidden layer, no squashing at the output layer, and arbitrary sigmoid activation function at the hidden layer are universal approximators: they are capable of arbitrarily accurate approximation to arbitrary mappings, provided sufficiently many hidden units are available. The present authors obtain identical conclusions but do not require the hidden-unit activation to be sigmoid. Instead, it can be a rather general nonlinear function. Thus, multilayer feedforward networks possess universal approximation capabilities by virtue of the presence of intermediate layers with sufficiently many parallel processors; the properties of the intermediate-layer activation function are not so crucial. In particular, sigmoid activation functions are not necessary for universal approximation.>
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Cites
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Cited by
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Approximation capabilities of multilayer feedforward networks
Hornik, K. (1991) · Neural Networks · doi:10.1016/0893-6080(91)90009-t · Cited by 6,091
Hornik, K., Stinchcombe, M., & White, H. (1990) · Neural Networks · doi:10.1016/0893-6080(90)90005-6 · Cited by 2,111
Extreme learning machines: a survey
Huang, G.-B., Wang, D. H., & Lan, Y. (2011) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-011-0019-y · Cited by 1,914
Approximation theory of the MLP model in neural networks
Pinkus, A. (1999) · Acta Numerica · doi:10.1017/s0962492900002919 · Cited by 1,529
Metadata
- Authors
- Stinchcombe, & White
- Venue
- International Joint Conference on Neural Networks
- Publisher
- IEEE
- Published
- 1989
- Pages
- 613-617 vol.1
- OpenAlex
- W2090270852
- Cited by
- 251
- References
- 2
- FWCI
- 14.14
- Type
- conference paper
- Language
- en
- Open access
- closed
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