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• W2043298855 abstract "On-line learning is examined for the radial basis function network, an important and practical type of neural network. The evolution of generalization error is calculated within a framework which allows the phenomena of the learning process, such as the specialization of the hidden units, to be analyzed. The distinct stages of training are elucidated, and the role of the learning rate described. The three most important stages of training, the symmetric phase, the symmetry-breaking phase, and the convergence phase, are analyzed in detail; the convergence phase analysis allows derivation of maximal and optimal learning rates. As well as finding the evolution of the mean system parameters, the variances of these parameters are derived and shown to be typically small. Finally, the analytic results are strongly confirmed by simulations." @default.
• W2043298855 created "2016-06-24" @default.
• W2043298855 creator A5079959902 @default.
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• W2043298855 date "1997-07-01" @default.
• W2043298855 modified "2023-10-16" @default.
• W2043298855 title "Dynamics of on-line learning in radial basis function networks" @default.
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• W2043298855 doi "https://doi.org/10.1103/physreve.56.907" @default.
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