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We show that the generalization ability of simple Perceptron-like devices is strongly enhanced by allowing the network itself to select the training examples. Analytic and numerical results are obtained for the Hebb and for the optimal Perceptron learning rule, respectively.
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Kinzel et al. (1990) studied this question.
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