A novel competitive learning algorithm for the parametric classification with Gaussian distributions

摘要

A competitive learning algorithm for the parametric classification of Gaussian sources is presented in this letter. The algorithm iteratively estimates the mean and prior probability of each class during the training. Bayes rule is then used for classification based on the estimated information. Simulation results show that the proposed algorithm outperforms k-means and LVQ algorithms for the parametric classification.