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.
I. Steinwart, Sparseness of Support Vector Machines -- Some Asymptotically Sharp Bounds. In Neural Information Processing Systems 16, pp. 1069-1076, (2004). Los Alamos National Laboratory Technical Report LA-UR-03-3643. [ Abstract | PostScript (121 KB) | PDF (98 KB) ]
