serial: Neural Network World vol.13, 6 (2003), p. 599-615
project(s): GA402/01/0981, GA ČR
keywords: neural networks, distribution mixtures, pattern recognition
Considering the statistical pattern recognition we approximate the unknown class-conditional probability distributions by multivariate Bernoulli mixtures. We show that both the parameter optimization based on EM algorithm and the resulting Bayesian decision-making can be realized by a strictly modular probabilistic neural network. The autonomous adaptation of neurons includes only the locally available information. The properties of the sequential learning procedure are illustrated by numerical examples.
Cosati: 09K, 12B, 06D