Sample Complexity for Learning Recurrent Perceptron Mappings

Part of Advances in Neural Information Processing Systems 8 (NIPS 1995)

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Bhaskar DasGupta, Eduardo Sontag


Recurrent perceptron classifiers generalize the classical perceptron model. They take into account those correlations and dependences among input coordinates which arise from linear digital filtering. This paper provides tight bounds on sample complexity associated to the fitting of such models to experimental data.