GRADIENT CRITERION METHOD FOR NEURAL NETWORKS AND APPLICATION TO TARGETED MARKETING

摘要

The present invention is drawn to a unique application of the Maximum Likelihood statistical method to commercial neural network technologies. The present invention utilizes the specific nature of the output in target marketing problems and makes it possible to produce more accurate and predictive results by minimizing a gradient criterion to produce model weights to get the maximum likelihood result. It is best used on "noisy" data and when one is interested in determining a distribution's overall accuracy, or best general description of reality.