A Bayesian Regularization Method for the Probabilistic RBF Network
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Performance Enhancement of RBF Networks in Classification by Removing Outliers in the Training Phase
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
A novel approach for distributed application scheduling based on prediction of communication events
Future Generation Computer Systems
Information Sciences: an International Journal
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We present a probabilistic neural network model, which is suitable for classification problems. This model constitutes an adaptation of the classical RBF network where the outputs represent the class conditional distributions. Since the network outputs correspond to probability densities functions, training process is treated as maximum likelihood problem and an Expectation-Maximization (EM) algorithm is proposed for adjusting the network parameters. Experimental results show that proposed architecture exhibits superior classification performance compared to the classical RBF network.