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Relevant features selection with radial basis neural networks

Radial basis neural networks are excellent candidates for selecting relevant features in pattern recognition problems. By a slight change in the traditional three layer architecture of a radial basis neural network, one can obtain two distinct methods, a qualitative and a quantitative one, which allows one to obtain a ranking within the features. The authors present two neural networks concepts, combining at the same time two different skills: classification and detection of relevant features in the input vector.