title: Predicting spatial data with RBF networks.
abstract: Spatial prediction needs to account for spatial information, which makes conventional radial basis function (RBF) networks inappropriate, for they assume independent and identical distribution. In this paper, we fuse spatial information at different layers of RBF. Experiments show fusion at hidden layer gives the best result and suggest that the optimal value is around one for the coefficient, which is used in the linear combination at the output layer.
authors.affiliation: Department of Computer Science, National University of Singapore, Singapore 117543, Singapore. hutianmi@comp.nus.edu.sg
authors.forename: Tianming
authors.initials: T
authors.lastname: Hu
authors.forename: Sam Yuan
authors.initials: SY
authors.lastname: Sung
mesh_terms: D000465:Algorithms; D002965:Classification; D016000:Cluster Analysis; D019277:Entropy; D016014:Linear Models; D016571:Neural Networks, Computer; D012043:Regression, Psychology