Perceptrons for photon-limited image classification
Marek Elbaum, Mark Syrkin · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
Perceptron learning for the Bayesian classification problem has been analyzed in the framework of Markov diffusion under the influence of competing stochastic forces. The analytic solution for the one-layer architecture yields an immediate relationship between the statistics of the input signal and the weight configuration built by the Perceptron. The computer simulation of the Perceptron learning classification of image-like patterns governed by the Poisson distribution demonstrates a dependence of the learning dynamics on the number of layers and the size of the training data set.