The statistical basis of neural network algorithms: Theory and applications

In a very general framework, neural network and related algorithms are discussed with emphasis on how the use of correlations between variables can improve on linear cuts. Mathematical results for a large class of neural algorithms are shown indicating the generic importance of high-order correlations between input variables. The dangers of the blind application of these newer techniques are stressed and suggestions are made for how to use them wisely.