Perceptron, Winnow, and PAC Learning

  title={Perceptron, Winnow, and PAC Learning},
  author={Rocco A. Servedio},
  journal={SIAM J. Comput.},
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