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Perceptron Mistake Bounds

We present a brief survey of existing mistake bounds and introduce novel bounds for the Perceptron or the kernel Perceptron algorithm. Our novel bounds generalize beyond standard margin-loss type bounds, allow for any convex and Lipschitz loss function, and admit a very simple proof.

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Works onCo-authorshipAuthorshipAuthorshipTopic signalWPerceptron Mistake Boundspreprint / 2013AMehryar MohriResearcherAAfshin RostamizadehResearcherTMachine Learning49008 works
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Perceptron Mistake Bounds

preprint / 2013

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