Generalization by weight-elimination with application to forecasting
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Neural networks and the bias/variance dilemma
Neural Computation
Neural Computation
A least third-order cumulants objective function
Neural Processing Letters
Neural Computation
A penalty-function approach for pruning feedforward neural networks
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
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A novel adaptive regularization parameter selection (ARPS) method is proposed in this paper to enhance the performance of the regularization method. The proposed ARPS method enables a gradient descent type training to tunnel through some of the undesired sub-optimal solutions on the composite error surface by means of changing the value of the regularization parameter. Undesired sub-optimal solutions are introduced inherently from regularized objective functions. Hence, the proposed ARPS method is capable of enhancing the regularization method without getting stuck at these sub-optimal solutions.