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Proceedings Paper

Random structure of error surfaces: toward new stochastic learning methods
Author(s): Andrew B. Kahng
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Paper Abstract

This paper gives an overview of current work which is directed toward verifying, and exploiting in practice, a recent scaling model for neural network error surfaces. We begin the next section by reviewing a model which describes Boltzmann learning as a stochastic search in the error surface. The discussion also reviews a potentially far-reaching fractal model of neural network error surfaces as instances of a class of high-dimensional fractional Brownian motions (fBm). The main body of the paper then describes a series of experimental results for object classification via noisy sensor data in a mine detection application.

Paper Details

Date Published: 1 July 1992
PDF: 12 pages
Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140136
Show Author Affiliations
Andrew B. Kahng, Univ. of California/Los Angeles (United States)

Published in SPIE Proceedings Vol. 1710:
Science of Artificial Neural Networks
Dennis W. Ruck, Editor(s)

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