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J. Shawe-Taylor and N. Cristianini (2000)

Margin Distribution and Soft Margin

In: Advances in Large Margin Classifiers, ed. by A.J. Smola and P.L. Bartlett and B. Schölkopf and D. Schuurmans, pp. 349-358, Cambridge, MA, MIT Press.

Rather than considering the minimum margin, the paper focuses on the margin distribution. The latter is a more robust quantity than the minimum margin itself which can be easily decreased by a single mislabeled example. In particular the authors provide generalization bounds, which motivate algorithms maximizing the minimum margin plus the $2$-norm of the slack variables for those patterns violating the margin condition. This is not the standard setting in SV machines which in general use the $1$-norm of the slacks, however, it coincides with the target function of optimization algorithms such as the one of Kowalczyk and can be useful in this regard.

by admin last modified 2007-01-31 11:08

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