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A Prior for Neural Networks utilizing Enclosing Spheres for Normalization

Neural Networks are famous for their advantageous flexibility for problems when there is insufficient knowledge to set up a proper model. On the other hand this flexibility can cause over‐fitting and can hamper the generalization properties of neural networks. Many approaches to regularize NN have been suggested but most of them based on ad‐hoc arguments. Employing the principle of transformation invariance we derive a general prior in accordance with the Bayesian probability theory for a class of feedforward networks. Optimal networks are determined by Bayesian model comparison verifying the applicability of this approach.