95-17 Sample Complexity for Learning Recurrent Perceptron Mappings

DIMACS TR: 95-17

Sample Complexity for Learning Recurrent Perceptron Mappings

Authors: Bhaskar Dasgupta, Eduardo Sontag

ABSTRACT
Recurrent perceptron classifies 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.

(This research was supported in part by US Air Force Grant AFOSR-94-0293)

Paper available at: ftp://dimacs.rutgers.edu/pub/dimacs/TechnicalReports/TechReports/1995/95-17.ps.gz

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