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NIPS
2003

Sparseness of Support Vector Machines---Some Asymptotically Sharp Bounds

8 years 11 months ago
Sparseness of Support Vector Machines---Some Asymptotically Sharp Bounds
The decision functions constructed by support vector machines (SVM’s) usually depend only on a subset of the training set—the so-called support vectors. We derive asymptotically sharp lower and upper bounds on the number of support vectors for several standard types of SVM’s. In particular, we show for the Gaussian RBF kernel that the fraction of support vectors tends to twice the Bayes risk for the L1-SVM, to the probability of noise for the L2-SVM, and to 1 for the LS-SVM.
Ingo Steinwart
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NIPS
Authors Ingo Steinwart
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