Luminita State, Catalina Cocianu
A Connectionist Approach of the Bayesian Pattern Classifier Adaptive Learning

Abstract.
The research reported in the paper aims at developing a suitable neural architecture for implementing the Bayesian procedure for solving pattern recognition problems. The proposed neural system is based on an inhibitive competition installed among the hidden neurons of the computation layer. The local memories of the hidden neurons are computed adaptively according to an estimation model of the parameters of the Bayesian classifier. Also, the paper reports a series of qualitative attempts to analyzing the behavior of a new learning procedure of the parameters of a hidden Markov model (HMM) dealing with different types of stochastic dependencies on the space of states corresponding to the underlying finite automaton. The approach aims at developing some new methods in processing image and speech signals in solving pattern recognition problems. Basically, the attempts are stated in terms of weighting processes and deterministic/non deterministic Bayesian procedures. The aims were mainly to derive asymptotical conclusions concerning the performance of the proposed estimation techniques in approximating the ideal Bayesian procedure. The proposed methodology adopts the standard assumptions on the conditional independence properties of the involved stochastic processes.

Keywords: Neural Networks, Competitive Learning, Hidden Markov Models, Pattern Classifier, Bayesian Learning, Weighting Processes, Markov Chains.