Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
An efficient MDL-based construction of RBF networks
Neural Networks
A global learing algorithm for a RBF network
Neural Networks
Visualizing multi-dimensional clusters, trends, and outliers using star coordinates
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Growing Compact RBF Networks Using a Genetic Algorithm
SBRN '02 Proceedings of the VII Brazilian Symposium on Neural Networks (SBRN'02)
INFOVIS '97 Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)
Fast learning in networks of locally-tuned processing units
Neural Computation
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The performance of neural networks is greatly affected by their design, yet the question of finding optimal designs remains open and inspires a considerable amount of research. Most of the researches have been focused on developing automatic algorithms for neural network configuration. This paper addresses the problem of Radial Basis Function Network (RBFN) design definition with a visual technique, called Star Coordinates. The purpose of this approach is to enable the RBFN design revision and refining process, capitalizing on the power of visualization and interactive operations.