Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23997
Title: Least third-order cumulant method with adaptive regularization parameter selection for neural networks
Authors: Leung, CT
Chow, TWS
Keywords: Generalization capability
Neural networks
Regularization
Third-order cumulant
Issue Date: 2001
Publisher: Elsevier Science Bv
Source: Artificial intelligence, 2001, v. 127, no. 2, p. 169-197 How to cite?
Journal: Artificial Intelligence 
Abstract: This paper introduces an interesting property of the least third-order cumulant objective function. The property is that the solution is optimal when the gradients of Mean Squares error and third-order cumulant error are zero vectors. The optimal solutions are independent of the value of regularization parameter λ. Also, an adaptive regularization parameter selection method is derived to control the convergences of Mean Squares error and the cumulant error terms. The proposed selection method is able to tunnel through the sub-optimal solutions, of which the locations are controllable, via changing the value of the regularization parameter. Consequently, the least third-order cumulant method with the adaptive regularization parameter selection method is theoretically capable of estimating an optimal solution when it is applied to regression problems.
URI: http://hdl.handle.net/10397/23997
ISSN: 0004-3702
DOI: 10.1016/S0004-3702(01)00061-3
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