Chinese Web page classification based on self-organizing mapping neural networks

This paper deals with self-organizing mapping (SOM) neural network's topology and learning algorithm, and the application in the automatic classification of Chinese Web pages. SOM neural network has the advantages of simple structure, ordered mapping topology and low complexity of learning. It is suitable for many complex problems such as multi-class pattern recognition, high dimension input vector and large quantity of training data. The accuracy of clustering can be improved when combining SOM's unsupervised learning algorithm with LVQ learning algorithm. At the end of the paper, it is proposed the classification result of SOM neural network applied in the 5087 html pages of People's Daily Web edition, with the average precision 90.08% and the average recall 89.85%.