Geometrical properties of nu support vector machines with different norms.
By employing the L1 or Linfinity 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.
Ikeda
Kazushi
K
Graduate School of Informatics, Kyoto University, Sakyo, Kyoto 606-8501 Japan. kazushi@i.kyoto-u.ac.jp
Murata
Noboru
N
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D006801:Humans; D007858:Learning / Q000502:physiology*; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated