The perceptron with dynamic margin
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
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We identify the classical perceptron algorithm with margin as a member of a broader family of large margin classifiers, which we collectively call the margitron. The margitron, (despite its) sharing the same update rule with the perceptron, is shown in an incremental setting to converge in a finite number of updates to solutions possessing any desirable fraction of the maximum margin. We also report on experiments comparing the margitron with decomposition support vector machines, cutting-plane algorithms, and gradient descent methods on hard margin tasks involving linear kernels which are equivalent to 2-norm soft margin. Our results suggest that the margitron is very competitive.