Random structure of error surfaces: toward new stochastic learning methods
Andrew B. Kahng · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
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.