From Margin to Sparsity. Graepel, T., Herbrich, R., & Williamson, R. C In Advances in Neural Information Processing Systems 13, pages 210--216, Denver, 2000. The MIT Press.
From Margin to Sparsity [pdf]Paper  abstract   bibtex   
We present an improvement of Novikoff's perceptron convergence theorem. Reinterpreting this mistake bound as a margin dependent sparsity guarantee allows us to give a PAC-style generalisation error bound for the classifier learned by the dual perceptron learning algorithm. The bound value crucially depends on the margin a support vector machine would achieve on the same data set using the same kernel. Ironically, the bound yields better guarantees than are currently available for the support vector solution itself.

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