The Perceptron with Dynamic Margin
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Publication:3093951
DOI10.1007/978-3-642-24412-4_18zbMATH Open1348.68199arXiv1105.6041OpenAlexW1639522494MaRDI QIDQ3093951
Author name not available (Why is that?)
Publication date: 19 October 2011
Published in: (Search for Journal in Brave)
Abstract: The classical perceptron rule provides a varying upper bound on the maximum margin, namely the length of the current weight vector divided by the total number of updates up to that time. Requiring that the perceptron updates its internal state whenever the normalized margin of a pattern is found not to exceed a certain fraction of this dynamic upper bound we construct a new approximate maximum margin classifier called the perceptron with dynamic margin (PDM). We demonstrate that PDM converges in a finite number of steps and derive an upper bound on them. We also compare experimentally PDM with other perceptron-like algorithms and support vector machines on hard margin tasks involving linear kernels which are equivalent to 2-norm soft margin.
Full work available at URL: https://arxiv.org/abs/1105.6041
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