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Sparsity of SVMs that use the epsilon-insensitive loss

Part of: Advances in Neural Information Processing Systems 21 (NIPS 2008)

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Abstract

In this paper lower and upper bounds for the number of support vectors are derived for support vector machines (SVMs) based on the epsilon-insensitive loss function. It turns out that these bounds are asymptotically tight under mild assumptions on the data generating distribution. Finally, we briefly discuss a trade-off in epsilon between sparsity and accuracy if the SVM is used to estimate the conditional median.