Sample Complexity for LearningRecurrent Perceptron
نویسندگان
چکیده
Recurrent perceptron classiiers generalize the classical perceptron model. They take into account those correlations and dependences among input coordinates which arise from linear digital ltering. This paper provides tight bounds on sample complexity associated to the tting of such models to experimental data.
منابع مشابه
Sample Complexity for Learning Recurrent Perceptron Mappings
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.
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تاریخ انتشار 1996