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• W1949535931 abstract "We revisit the oft-studied asymptotic (in sample size) behavior of the parameter or weight estimate returned by any member of a large family of neural network training algorithms. By properly accounting for the characteristic property of neural networks that their empirical and generalization errors possess multiple minima, we establish conditions under which the parameter estimate converges strongly into the set of minima of the generalization error. These results are then used to derive learning curves for generalization and empirical errors that leads to bounds on rates of convergence." @default.
• W1949535931 created "2016-06-24" @default.
• W1949535931 creator A5005040383 @default.
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• W1949535931 date "2002-11-27" @default.
• W1949535931 modified "2023-09-26" @default.
• W1949535931 title "Parameter convergence and learning curves for neural networks" @default.
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• W1949535931 doi "https://doi.org/10.1109/isit.1998.708991" @default.
• W1949535931 hasPublicationYear "2002" @default.
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