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, train a cosine RBFNN based on the gradient descent learning process. Also, we apply this new method for classification of deep Web sources. Experimental results show that the average Precision, Recall and F of our ESIC-based RBFNN classifier achieve higher performance than BP, Support Vector Machines (SVM) and OLS RBF for our deep Web sources classification problems.
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Feng, Y., Wu, Z., Zhong, J. et al. An enhanced swarm intelligence clustering-based RBFNN classifier and its application in deep Web sources classification. Front. Comput. Sci. China 4, 560–570 (2010). https://doi.org/10.1007/s11704-010-0104-5
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DOI: https://doi.org/10.1007/s11704-010-0104-5