Sparseness of Support Vector Machines---Some Asymptotically Sharp Bounds
نویسنده
چکیده
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. Our results significantly improve recent achievments of the author.
منابع مشابه
Sparseness of Support Vector Machines
Support vector machines (SVMs) construct decision functions that are linear combinations of kernel evaluations on the training set. The samples with non-vanishing coefficients are called support vectors. In this work we establish lower (asymptotical) bounds on the number of support vectors. On our way we prove several results which are of great importance for the understanding of SVMs. In parti...
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تاریخ انتشار 2003