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- W2104414247 abstract "A theoretical approach to the determination of the number of training examples for a neural network architecture is provided by the theory of Vapnik and Chervonenkis on the minimization of the empirical risk. We report here a new bound on the joint probability that both the approximation error between the binary function learned by the input/output examples and the target binary function is larger than /spl epsiv/ and the empirical error on the examples is smaller than a fixed non-null fraction of /spl epsiv/. The given bounds are independent of the probability distribution on the input space and improve some existing results on the generalization abilities of an adaptive binary function." @default.
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- W2104414247 date "2002-11-22" @default.
- W2104414247 modified "2023-09-23" @default.
- W2104414247 title "New bounds for correct generalization" @default.
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- W2104414247 doi "https://doi.org/10.1109/icnn.1997.616173" @default.
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