Simultaneous input variable and basis function selection for RBF networks

Abstract

Input selection is advantageous in regression problems. For example, it might decrease the training time of models, reduce measurement costs, and circumvent problems of high dimensionality. Inclusion of useless inputs into the model increases also the likelihood of overfitting. Neural networks provide good generalization in many cases, but their… (More)
DOI: 10.1016/j.neucom.2008.10.003

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