High Performance Text Categorization System Based on a Novel Neural Network Algorithm

This paper describes a novel approach for text categorization based on the improved Backpropagation neural network (BPNN). BPNN has been widely used in classification and pattern recognition. However it has some generally acknowledged defects, such as slow convergence and easy to enter into local minima. In this paper, we introduce an improved BPNN that can overcome these defects. We tested the improved model on the standard Reuter-21578, and the result shows that the proposed model is able to achieve high categorization effectiveness as measured by the precision, recall and F-measure.