Sparseness of Support Vector Machines-Some Asymptotically Sharp Bounds

摘要

The decision functions constructed by support vector machines (SVMs) usually depend only on a subset of the training setthe so-called support vectors. We derive asymptotically sharp lower and upper bounds on the number of support vectors for several standard types of SVMs. Our results significantly improve recent achievments of the author.