Geometrical Properties of Lifting-Up in the Nu Support Vector Machines(Biocybernetics, Neurocomputing)

スポンサーリンク

概要

• 論文の詳細を見る

• Geometrical properties of the lifting-up technique in support vector machines (SVMs) are discussed here. In many applications, an SVM finds the optimal inhomogeneous separating hyperplane in terms of margins while some of the theoretical analyses on SVMs have treated only homogeneous hyperplanes for simplicity. Although they seem equivalent due to the so-called lifting-up technique, they differ in fact and the solution of the homogeneous SVM with lifting-up strongly depends on the parameter of lifting-up. It is also shown that the solution approaches that of the inhomogeneous SVM in the asymptotic case that the parameter goes to infinity.

• 社団法人電子情報通信学会の論文
• 2006-02-01

著者

• Ikeda Kazushi

  Kyoto Univ. Kyoto‐shi Jpn

関連論文

• A Network Analysis of Genetic Algorithms(Biocybernetics, Neurocomputing)
• Geometric Properties of Quasi-Additive Learning Algorithms(Control, Neural Networks and Learning,Nonlinear Theory and its Applications)
• Geometrical Properties of Lifting-Up in the Nu Support Vector Machines(Biocybernetics, Neurocomputing)

スポンサーリンク