EFFICIENCY OF MULTILAYERED FEEDFORWARD NEURAL NETWORKS ON CLASSIFICATION IN RELATION TO LINEAR DISCRIMINANT-ANALYSIS, QUADRATIC DISCRIMINANT-ANALYSIS AND REGULARIZED DISCRIMINANT-ANALYSIS

The efficiency of multi-layered feed-forward networks (MLF) on classification is evaluated by applying them to simulated data. The classes are normal multivariate with three different structures for the matrix of covariance. For each of them a complete factorial design, 2(3), was performed, with a replicated central point in order to study the effect of the relationships objects-variables, noise-signal and distance between centroids. The results were compared to those obtained by applying linear discriminant analysis, quadratic discriminant analysis and regularized discriminant analysis to the same sets of data. The comparison was carried out by an ANOVA of the experimental designs and by principal components and correspondence analysis.