Abstract
Neural networks as non-parametric classification statistical tools. The relationships between neural networks and statistical methods have been recently analysized (see e.g. Cherkassky, F riedman and Wechler, 1994; Ripley, 1996). In general terms neural networks have shown and equal or greater capacity to classify than statistical tools. Morever they do not need to satisfy the parametric asumptions of the stistical techniques. Simulations about simulated and real data are shown: multi-layer perceptrons versus logistic regresion and discriminant analysis statistical models are compared in classifications tasks, manipulating the correlation patterns within input variables (predictors) and between the input variables with the output variable (criterion). Results show that neural networks classify better than statistical tools both in simulated data as in empirical data (González-Romá et al, 1999).
