With the capabilities of parallel computation, distributed processing, and fault tolerance neural networks are employed widely in a number of research fields. Among the models of neural networks the single-layer perceptron and the multi-layer perceptron are the most popular ones used in supervised learning problems. However, there exists the redundant nodes that are insignificant for classification no matter which one of the two networks is trained to be a classifier. Although a net of a larger size usually has a faster learning rate, it results in an increase of forward computation complexity in either pattern recognizing or system relearning. In this paper, a new sequential classification model based on neural network is proposed. The model which combines the advantages of neural networks with the properties of the sequential classification is shown to have an encouraging performance for net pruning and feature reduction. In the experiments, two-class and m-class (m > 2) problems are implemented to prove the practicability of the new technique with a balance between the accuracy of pattern classification and the size of networks. In the conclusion, an overall discussion of the proposed model and technical comparisons with previous related research issues on net pruning are given.

Date Published: 1 July 1992
PDF: 12 pages
Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140077

Published in SPIE Proceedings Vol. 1710:
Science of Artificial Neural Networks
Dennis W. Ruck, Editor(s)