# Perceptron, Winnow, and PAC Learning

@article{Servedio2002PerceptronWA, title={Perceptron, Winnow, and PAC Learning}, author={Rocco A. Servedio}, journal={SIAM J. Comput.}, year={2002}, volume={31}, pages={1358-1369} }

- Published 2002 in SIAM J. Comput.
DOI:10.1137/S0097539798340928

We analyze the performance of the widely studied Perceptron andWinnow algorithms for learning linear threshold functions under Valiant’s probably approximately correct (PAC) model of concept learning. We show that under the uniform distribution on boolean examples, the Perceptron algorithm can efficiently PAC learn nested functions (a class of linear threshold functions known to be hard for Perceptron under arbitrary distributions) but cannot efficiently PAC learn arbitrary linear threshold… CONTINUE READING