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Abstract: 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 overfitting 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... (Update)
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BibTeX entry: (Update)
@misc{ toussaint-prior,
author = "U. v. Toussaint and S. Gori and V. Dose",
title = "A Prior for Neural Networks Utilizing Enclosing Spheres for Normalization",
url = "citeseer.ist.psu.edu/729807.html" }
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1 Bayesian training of backpropagation networks (context) - NEAL - 1992
1 Hyperplane priors in Bayesian Inference and Maximum Entropy .. (context) - DOSE - 2003
1 Bayesian approach for neural networks (context) - LAMPINEN, VEHTARI - 2001
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