Sparsity of SVMs that use the epsilon-insensitive loss

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

Authors

Ingo Steinwart, Andreas Christmann

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.