Bayes classifier based on self-organizing mapping neural network

This paper proposes a new self-organizing learning algorithm based on isoton mapping characteristic and cluster characteristic of self-organizing mapping neural network for the Bayes classification. The algorithm is that the network is trained by the given pattern samples, so that the classification results are directly presented from the output, avoiding the errors introduced by the estimation probability density function. The experimental results show the efficiency and reliability of this algorithm