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- W1981056097 abstract "In this paper, two improved constrained learning algorithms that are able to guarantee to obtain better generalization performance are proposed. These two algorithms are substantially on-line learning ones. The cost term for the additional functionality of the first improved algorithm is selected based on the first-order derivatives of the neural activation at hidden layers, while the one of the second improved algorithm is selected based on second-order derivatives of the neural activation at hidden layers and output layer. In the course of training, the cost terms selected from these additional cost functions can penalize the input-to-output mapping sensitivity or high-frequency components included in training data. Finally, theoretical justifications and simulation results are given to verify the efficiency and effectiveness of the two proposed learning algorithms." @default.
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- W1981056097 date "2006-03-01" @default.
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- W1981056097 title "Improved constrained learning algorithms by incorporating additional functional constraints into neural networks" @default.
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- W1981056097 doi "https://doi.org/10.1016/j.amc.2005.04.073" @default.
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