Asymptotic Level Density for a Class of Vector Quantization Processes

Ritter H (1991)
IEEE Transactions on Neural Networks 2(1): 173-175.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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DOI
Autor*in
Ritter, HelgeUniBi
Einrichtung
Technische Fakultät > AG Neuroinformatik
Abstract / Bemerkung
It is shown that for a class of vector quantization processes, related to neural modeling, that the asymptotic density Q(x ) of the quantization levels in one dimension in terms of the input signal distribution P(x) is a power law Q(x)=C-P(x)α , where the exponent α depends on the number n of neighbors on each side of a unit and is given by α=2/3-1/(3n 2+3[n+1]2). The asymptotic level density is calculated, and Monte Carlo simulations are presented.
Erscheinungsjahr
1991
Zeitschriftentitel
IEEE Transactions on Neural Networks
Band
2
Ausgabe
1
Seite(n)
173-175
ISSN
1045-9227
Page URI
https://pub.uni-bielefeld.de/record/2715364

Zitieren

Ritter H. Asymptotic Level Density for a Class of Vector Quantization Processes. IEEE Transactions on Neural Networks. 1991;2(1):173-175.
Ritter, H. (1991). Asymptotic Level Density for a Class of Vector Quantization Processes. IEEE Transactions on Neural Networks, 2(1), 173-175. doi:10.1109/72.80310
Ritter, Helge. 1991. “Asymptotic Level Density for a Class of Vector Quantization Processes”. IEEE Transactions on Neural Networks 2 (1): 173-175.
Ritter, H. (1991). Asymptotic Level Density for a Class of Vector Quantization Processes. IEEE Transactions on Neural Networks 2, 173-175.
Ritter, H., 1991. Asymptotic Level Density for a Class of Vector Quantization Processes. IEEE Transactions on Neural Networks, 2(1), p 173-175.
H. Ritter, “Asymptotic Level Density for a Class of Vector Quantization Processes”, IEEE Transactions on Neural Networks, vol. 2, 1991, pp. 173-175.
Ritter, H.: Asymptotic Level Density for a Class of Vector Quantization Processes. IEEE Transactions on Neural Networks. 2, 173-175 (1991).
Ritter, Helge. “Asymptotic Level Density for a Class of Vector Quantization Processes”. IEEE Transactions on Neural Networks 2.1 (1991): 173-175.

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