Random structure of error surfaces: toward new stochastic learning methods

Andrew B. Kahng

doi:10.1117/12.140136

1 July 1992 Random structure of error surfaces: toward new stochastic learning methods

Andrew B. Kahng

Proceedings Volume 1710, Science of Artificial Neural Networks; (1992) https://doi.org/10.1117/12.140136
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

Andrew B. Kahng "Random structure of error surfaces: toward new stochastic learning methods", Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); https://doi.org/10.1117/12.140136

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