PAC Analogues of Perceptron and Winnow Via Boosting the Margin

Abstract

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 sample 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… (More)
DOI: 10.1023/A:1013633619373

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