Abstract
The central problem in training a radial basis function neural network (RBFNN) is the selection of hidden layer neurons, which includes the selection of the center and width of those neurons. In this paper, we propose an enhanced swarm intelligence clustering (ESIC) method to select hidden layer neurons, and then, training a cosine RBFNN base on gradient descent learning process. Also, the new method is applied for web text classification. Experimental results show that the average Accuracy, Precision and Recall of our ESIC-based RBFNN classifier maintained a better performance than BP, SVM and OLS RBF.
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Feng, Y., Wu, Z., Zhong, J., Ye, C., Wu, K. (2009). An Enhanced Swarm Intelligence Clustering-Based RBF Neural Network Web Text Classifier. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_77
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DOI: https://doi.org/10.1007/978-3-642-01510-6_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01509-0
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