UM
Regularization parameter estimation for feedforward neural networks
Guo P.1; Lyu M.R.2; Chen C.L.P.3
2003
Source PublicationIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ISSN10834419
Volume33Issue:1Pages:35-44
AbstractUnder the framework of the Kullback-Leibler (KL) distance, we show that a particular case of Gaussian probability function for feedforward neural networks (Nns) reduces into the first-order Tikhonov regularizer. The smooth parameter in kernel density estimation plays the role of the regularization parameter. Under some approximations, an estimation formula is derived for estimating regularization parameters based on training data sets. The similarity and difference of the obtained results are compared with other's work. Experimental results show that the estimation formula works well in the sparse and small training sample cases.
KeywordRegularization parameter estimation Small training data set Tikhonov regularizer
DOI10.1109/TSMCB.2003.808176
URLView the original
Language英語
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Cited Times [WOS]:56   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Beijing Normal University
2.Chinese University of Hong Kong
3.University of Texas at San Antonio
Recommended Citation
GB/T 7714
Guo P.,Lyu M.R.,Chen C.L.P.. Regularization parameter estimation for feedforward neural networks[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics,2003,33(1):35-44.
APA Guo P.,Lyu M.R.,&Chen C.L.P..(2003).Regularization parameter estimation for feedforward neural networks.IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics,33(1),35-44.
MLA Guo P.,et al."Regularization parameter estimation for feedforward neural networks".IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 33.1(2003):35-44.
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