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Oriented non-radial basis functions for image coding and analysis
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Neural Networks: A Comprehensive Foundation
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Credit Analysis Using Radial Basis Function Networks
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Optimal adaptive k-means algorithm with dynamic adjustment of learning rate
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Data Fusion in Radial Basis Function Networks for Spatial Regression
Neural Processing Letters
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ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
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ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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Clustering techniques have a strong influence on the performance achieved by Radial Basis Function (RBF) networks. This article compares the performance achieved by RBF networks using seven different clustering techniques. For such, different sizes of RBF networks are trained and tested using an Automatic Target Recognition data set. The performances of these RBF networks using each clustering technique are compared and analyzed. This article also evaluates how the performance can be improved by combining RBF networks, training with different clustering techniques, in committees.