The dynamics of collective sorting robot-like ants and ant-like robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Computer
Ant colony optimization of clustering models: Research Articles
International Journal of Intelligent Systems
Automatic complex schema matching across Web query interfaces: A correlation mining approach
ACM Transactions on Database Systems (TODS)
Communications of the ACM - ACM at sixty: a look back in time
Network Anomaly Detection Based on DSOM and ACO Clustering
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Domain-Specific Deep Web Sources Discovery
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 05
Application of ant K-means on clustering analysis
Computers & Mathematics with Applications
Reformulated radial basis neural networks trained by gradient descent
IEEE Transactions on Neural Networks
An ART-based construction of RBF networks
IEEE Transactions on Neural Networks
A self-organizing HCMAC neural-network classifier
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
IEEE Transactions on Neural Networks
Neuron selection for RBF neural network classifier based on data structure preserving criterion
IEEE Transactions on Neural Networks
RBF neural network based on q-Gaussian function in function approximation
Frontiers of Computer Science in China
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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.