NIPS Proceedingsβ

Sparsity of SVMs that use the epsilon-insensitive loss

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

[PDF] [BibTeX]



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.