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D. B. Schwartz
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Exhaustive Learning
UnavailablePublisher: Journals Gateway
Neural Computation (1990) 2 (3): 374–385.
Published: 01 September 1990
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Exhaustive exploration of an ensemble of networks is used to model learning and generalization in layered neural networks. A simple Boolean learning problem involving networks with binary weights is numerically solved to obtain the entropy S m and the average generalization ability G m as a function of the size m of the training set. Learning curves G m vs m are shown to depend solely on the distribution of generalization abilities over the ensemble of networks. Such distribution is determined prior to learning, and provides a novel theoretical tool for the prediction of network performance on a specific task.