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A forward constrained selection algorithm for probabilistic neural network

Zong, N. and Hong, X. (2007) A forward constrained selection algorithm for probabilistic neural network. Lecture Notes in Computer Science, 4492. pp. 699-704. ISSN 0302-9743 9783540723929

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Abstract/Summary

A new probabilistic neural network (PNN) learning algorithm based on forward constrained selection (PNN-FCS) is proposed. An incremental learning scheme is adopted such that at each step, new neurons, one for each class, are selected from the training samples arid the weights of the neurons are estimated so as to minimize the overall misclassification error rate. In this manner, only the most significant training samples are used as the neurons. It is shown by simulation that the resultant networks of PNN-FCS have good classification performance compared to other types of classifiers, but much smaller model sizes than conventional PNN.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15500
Additional Information:Proceedings Paper 4th International Symposium on Neural Networks (ISNN 2007) JUN 03-07, 2007 Nanjing, PEOPLES R CHINA

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