Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

November 1, 2005, Vol. 17, No. 11, Pages 2508-2529
(doi: 10.1162/0899766054796897)
© 2005 Massachusetts Institute of Technology
Geometrical Properties of Nu Support Vector Machines with Different Norms
Article PDF (185.63 KB)
Abstract

By employing the L1 or L norms in maximizing margins, support vector machines (SVMs) result in a linear programming problem that requires a lower computational load compared to SVMs with the L2 norm. However, how the change of norm affects the generalization ability of SVMs has not been clarified so far except for numerical experiments. In this letter, the geometrical meaning of SVMs with the Lp norm is investigated, and the SVM solutions are shown to have rather little dependency on p.