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Perceptron Mistake Bounds
Published 1 May 2013 in cs.LG | (1305.0208v2)
Abstract: 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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