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• W2149759174 abstract "Two popular hazards in supervised learning of neural networks are local minima and over fitting. Application of the momentum technique dealing with the local optima has proved efficient but it is vulnerable to over fitting. In contrast, deployment of the early stopping technique might overcome the over fitting phenomena but it sometimes terminates into the local minima. This paper proposes a hybrid approach, which is a combination of two processing neurons: momentum and early stopping, to tackle these hazards, aiming at improving the performance of neural networks in terms of both accuracy and processing time in function approximation. Experimental results conducted on various kinds of non-linear functions have demonstrated that the proposed approach is dominant compared with conventional learning approaches." @default.
• W2149759174 created "2016-06-24" @default.
• W2149759174 creator A5041931683 @default.
• W2149759174 date "2010-10-01" @default.
• W2149759174 modified "2023-09-27" @default.
• W2149759174 title "Smoothing Supervised Learning of Neural Networks for Function Approximation" @default.
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• W2149759174 doi "https://doi.org/10.1109/kse.2010.15" @default.
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