Quasi-linear support vector machine for nonlinear classification

Bo Zhou, Benhui Chen, Jinglu Hu*

*この研究の対応する著者

研究成果査読

19 被引用数 (Scopus)

抄録

This paper proposes a so called quasi-linear support vector machine (SVM), which is an SVM with a composite quasi-linear kernel. In the quasi-linear SVM model, the nonlinear separation hyperplane is approximated by multiple local linear models with interpolation. Instead of building multiple local SVM models separately, the quasi-linear SVM realizes the multi local linear model approach in the kernel level. That is, it is built exactly in the same way as a single SVM model, by composing a quasi-linear kernel. A guided partitioning method is proposed to obtain the local partitions for the composition of quasi-linear kernel function. Experiment results on artificial data and benchmark datasets show that the proposed method is effective and improves classification performances.

本文言語English
ページ(範囲)1587-1594
ページ数8
ジャーナルIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
E97-A
7
DOI
出版ステータスPublished - 2014 7

ASJC Scopus subject areas

  • 信号処理
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 電子工学および電気工学
  • 応用数学

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