Backestimation for training multilayer perceptrons

The training of a multilayer perceptron is formulated as a maximum likelihood estimation problem. Statistical techniques such as the EM algorithm and the linear regression are used to exploit the linearity and separability structures of the feedforward multilayer neural networks. The resulting algorithm iterates a few linear regressors and nonlinear data transformers. All its parameters are statistically determined. Initial numerical experiments show that the algorithm has outstanding performance and deserves to be fully developed