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. |