Anderson Discriminant Function Instead of an Artificial Neural Network for Evaluation of Class Posterior Probabilities

We propose a non-parametric approximation of the Anderson discriminant function and class posterior probability for solving classification problems. It is necessary to choose only one parameter of the weight function, in contrast to the many parameters used to solve the classification problem by the artificial neural network method.