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A Priori Estimates of the Generalization Error for Two-layer Neural Networks. (arXiv:1810.06397v1 [stat.ML])
来源于:arXiv
New estimates for the generalization error are established for the two-layer
neural network model. These new estimates are a priori in nature in the sense
that the bounds depend only on some norms of the underlying functions to be
fitted, not the parameters in the model. In contrast, most existing results for
neural networks are a posteriori in nature in the sense that the bounds depend
on some norms of the model parameters. The error rates are comparable to that
of the Monte Carlo method for integration problems. Moreover, these bounds are
equally effective in the over-parametrized regime when the network size is much
larger than the size of the dataset. 查看全文>>