Share Email Print

Proceedings Paper

Random structure of error surfaces: toward new stochastic learning methods
Author(s): Andrew B. Kahng
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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)

© SPIE. Terms of Use
Back to Top