Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/34125
Title: Improving bayesian regularization of ANN via pre-training with early-stopping
Authors: Chan, ZSH
Ngan, HW
Rad, AB
Keywords: Artificial neural networks
Bayesian regularization
Early stopping algorithm
Issue Date: 2003
Publisher: Kluwer Academic Publ
Source: Neural processing letters, 2003, v. 18, no. 1, p. 29-34 How to cite?
Journal: Neural Processing Letters 
Abstract: We propose a simple method that enhances the performance of Bayesian Regularization of Artificial Neural Network (ANN) through pre-training of initial network with the Early-Stopping algorithm. The proposed method is applied to the regularization of Feedforward Neural Networks to regress three benchmark data series. Significant reduction in both the cross-validation error and the number of training over standard Bayesian Regularisation is achieved.
URI: http://hdl.handle.net/10397/34125
ISSN: 1370-4621
DOI: 10.1023/A:1026271406135
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