Stability evaluation of neural and Bayesian classifiers: A new insight

Referring to the statistical point of view, we present in this work, a new criterion for evaluating neural networks stability compared to the Bayesian classifier. The stability comparison is performed by the error rate probability densities function estimated by the kernel-diffeomorphism semi-bounded Plug-in algorithm. The Bayesian and combination approaches for neural networks improve the performance and stability degree of the classical neural classifiers.