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Proceedings Paper

Constructing near-tight wavelet frames by neural networks
Author(s): Xin Li
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Paper Abstract

Suppose that (sigma) is a sigmoidal function which is the activation function of a neural network. Under certain assumptions on the derivatives of (sigma) , we show that a simple linear combination of dilates and translates of (sigma) generates a near tight wavelet frame for L2(R), which is then used in constructing approximation to multivariate functions by neural networks with one hidden layer.

Paper Details

Date Published: 23 October 1996
PDF: 11 pages
Proc. SPIE 2825, Wavelet Applications in Signal and Image Processing IV, (23 October 1996); doi: 10.1117/12.255226
Show Author Affiliations
Xin Li, Univ. of Nevada/Las Vegas (United States)

Published in SPIE Proceedings Vol. 2825:
Wavelet Applications in Signal and Image Processing IV
Michael A. Unser; Akram Aldroubi; Andrew F. Laine, Editor(s)

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