Boundedness of Input Space and Effective Dimension of Feature Space in Kernel Methods (Biocybernetics, Neurocomputing)

スポンサーリンク

概要

• 論文の詳細を見る

• Kernel methods such as the support vector machines map input vectors into a high-dimensional feature space and linearly separate them there. The dimensionality of the feature space depends on a kernel function and is sometimes of an infinite dimension. The Gauss kernel is such an example. We discuss the effective dimension of the feature space with the Gauss kernel and show that it can be approximated to a sum of polynomial kernels and that its dimensionality is determined by the boundedness of the input space by considering the Taylor expansion of the kernel Gram matrix.

• 社団法人電子情報通信学会の論文
• 2004-01-01

著者

• Ikeda Kazushi

  Graduate School Of Informatics Kyoto University

• Ikeda K

  Graduate School Of Informatics Kyoto University

関連論文

• Boundedness of Input Space and Effective Dimension of Feature Space in Kernel Methods (Biocybernetics, Neurocomputing)
• An asymptotic statistical analysis of support vector machines with soft margins
• Low-Dimensional Feature Representation for Instrument Identification

スポンサーリンク