We describe a novel family of PAC model algorithms for learning linear threshold functions. The new algorithms work by boosting a simple weak learner and exhibit complexity bounds remarkably similar to those of known online algorithms such as Perceptron and Winnow, thus suggesting that these well-studied online algorithms in some sense correspond to instances of boosting. We show that the new algorithms can be viewed as natural PAC analogues of the online $p$-norm algorithms which have recently been studied by Grove, Littlestone, and Schuurmans \cite{GLS} and Gentile and Littlestone \cite{GL}. As special cases of the algorithm, by taking $p=2$ and $p=\infty$ we obtain natural boosting-based PAC analogues of Perceptron and Winnow respectively. The $p = \infty$ case of our algorithm can also be viewed as a generalization (with an improved sample complexity bound) of Jackson and Craven's PAC-model boosting-based algorithm for learning ``sparse perceptrons'' \cite{JC}. The analysis of the generalization error of the new algorithms relies on techniques from the theory of large margin classification.
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