Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Convergence of an EM-type algorithm for spatial clustering
Pattern Recognition Letters
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Combining RBF Networks Trained by Different Clustering Techniques
Neural Processing Letters
Spatial Data Mining: A Database Approach
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Signal detection using the radial basis function coupled map lattice
IEEE Transactions on Neural Networks
Local neural networks of space-time predicting modeling for lattice data in GIS
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Modified radial basis function network for brain tumor classification
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
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Conventional radial basis function (RBF) networks for spatial regression assume independent and identical distribution and ignore spatial information. In contrast to input fusion, we push spatial information further into RBF networks by fusing output from hidden and output layers. Three case studies demonstrate the advantage of hidden fusion over others and indicate the optimal value is around 1 for the coefficient used in hidden fusion, which links the output from the hidden layer for each site with their neighbors.