Perceptron Mistake Bounds
Machine Learning
2013-07-24 v2
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.
Cite
@article{arxiv.1305.0208,
title = {Perceptron Mistake Bounds},
author = {Mehryar Mohri and Afshin Rostamizadeh},
journal= {arXiv preprint arXiv:1305.0208},
year = {2013}
}
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