Works in the Atlas cited by this entry.
Raw extraction retains OCR errors, ligatures, page headers and column-order artifacts. Entries have not all been normalized or individually verified.
Read the indexed bibliography
Reference section 1 (PDF pages 8)
Billingsley, P. (1979). Probability and measure. New York: Wiley. Cybenko, G. (1988). Approximation by superpositions of a sig-
moidaifunction (Tech. Rep. No. 856). Urbana, IL: University of Illinois Urbana-Champaign Department of Electrical and Computer Engineering. Dugundji, J. (1966). Topology. Boston: Allyn and Bacon, Inc. Gallant, A. R., &White, H. (1988). There exists a neural network that does not make avoidable mistables. In IEEE Second International Conference on Neural Networks (pp. 1:657-X164). San Diego: SOS Printing. Grenander, U. (1981). Abstract inference. New York: Wiley. Halmos, P. R. (1974). Measure theory. New York: Springer-Verlag. Hecht-Nielsen, R. (1987). Kolmogorov’s mapping neural network existence theorem. In IEEE First International Conference on Neural Networks (pp. III:ll-14). San Diego: SOS Printing. Hecht-Nielsen, R. (1989). Theory of the back propagation neural network. In Proceedings of the International Joint Conference on Neural Networks (pp. 1593408). San Diego: SOS Printing. Hornik, K., Stinchcombe, M., & White, H. (1988). Multilayer feedforward networks are universal approximators (Discussion Paper 88-45). San Diego, CA: Department of Economics, University of California, San Diego. IEEE First International Conference on Neural Networks (1987). M. Caudill and C. Butler (Eds.). San Diego: SOS Printing. IEEE Second International Conference on Neural Networks (1988). San Diego: SOS Printing. Irie, B., & Miyake, S. (1988). Capabilities of three layer perceptrans. In IEEE Second International Conference on Neural Networks (pp. 1641-648). San Diego: SOS Printing Kolmogorov, A. N. (1957). On the representation of continuous
K. Hornik, M. Stinchcornhe, und H. White
functions of many variables by superposition of continuous functions of one variable and addition. Doklady Akademii Nauk SSR, 114, 953-956. Kolmogorov, A. N.. & Tihomirov. V. M. (1961). E-entropy and e-capacity of sets in functional spaces. American Mathematical Society Translations, 2(17), 277-364. Lapedes. A., & Farber. R. (1988). How neural networks work (Tech. Rep. LA-UR-88-418). Los Alamos. NM: Los Alamos National Laboratory. le Cun, Y. (1987). Modeles connexiontstes de l’upprentissage. IIese de Doctorat, Universite Pierre et Marie Curie. Lorentz, G. G. (1976). The thirteenth problem of Hilbert. In F E. Browder (Ed.), Proceedings of Symposia in Pure Mathematics (Vol. 28, pp. 419-430). Providence. RI: American Mathematical Society. Maxwell. T., Giles, G. L.. Lee, Y. C., & Chen, H. H. (1986). Nonlinear dynamics of artificial neural systems. In J. Denker (Ed.), Neural networks for computing. New York: American Institute of Physics. Minsky, M., & Papert, S. (1969). Perceprrons. Cambridge: MIT Press. Rudin. W. (1964). Principles of mathematrcal analysts. New York: McGraw-Hill. Severini, J. A., & Wang, W. H. (1987). C.‘onvergence rates of maximum likelihood and related estimates in general parameter vpaces (Working Paper). Chicago. IL: l!niversity of Chicago Department of Statistics. Stinchcombe, M., & White, H. (1989). Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions. In Proceedings off the international Joint Conference on Neural Networks (pp. 1:613-618). San Diego: SOS Printing. White, H. (1988a). The case for conceptual and operational separation of network architectures and learning mechanisms (Discussion Paper 88-21). San Diego, CA: Department of Economics, University of California, San Diego. White, H. (1988b). Multilayer feedforward networks can learn arbitrary mappings: Connectionist nonparametric regression with automatic and semi-automatic determination of network complexity (Discussion Paper). San Diego, CA: Department of Economics. University of California, San Diego. White, H., & Wooldridge, J. M. (in press). Some results for sieve estimation with dependent observations. In W. Barnett. J. Powell. & G. Tauchen (Eds.), Nonparametric and semi-parametric methods in econometrics and statistus. New York: Cambridge University Press. Williams, R. J. (1986). The logic of activation functions. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing: Explorations in the microstructures of cognition (Vol. 1, pp. 423-443). Cambridge: MIT Press.
Comments
Discuss this research, ask a question, or suggest a correction. Comments appear after the site owner approves them.
Moderate comments
Loading comments…
Sign in with ChatGPT to comment
Use your OpenAI account. Published comments show the display name you choose, not your account email.