A simplified statistical analysis of feedforward neural classifier
We propose an analytical procedure to determine the parameters of a neural classifier. A cost function based on the input statistics is defined. We also suggest an approximation model to describe the output distribution of each nonlinear unit with Gaussian inputs. We show that the resulting parametric classifier can perform as well as a conventional neural classifier trained with the backpropagation algorithm to minimize the mean square error.<<ETX>>