Advances in neural information processing systems 2
Neural Computation
A practical Bayesian framework for backpropagation networks
Neural Computation
Linear Dependency between epsilon and the Input Noise in epsilon-Support Vector Regression
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Neural input selection-A fast model-based approach
Neurocomputing
Regularization of fuzzy cognitive maps for hybrid decision support system
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
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Inspired by the recent upsurge of interest in Bayesian methods we consider adaptive regularization. A generalization based scheme for adaptation of regularization parameters is introduced and compared to Bayesian regularization. We show that pruning arises naturally within both adaptive regularization schemes. As model example we have chosen the simplest possible: estimating the mean of a random variable with known variance. Marked similarities are found between the two methods in that they both involve a “noise limit,” below which they regularize with infinite weight decay, i.e., they prune. However, pruning is not always beneficial. We show explicitly that both methods in some cases may increase the generalization error. This corresponds to situations where the underlying assumptions of the regularizer are poorly matched to the environment.