Quasi-linear support vector machine for nonlinear classification

Bo Zhou, Benhui Chen, Jinglu Hu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1587-1594
Number of pages8
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Issue number7
Publication statusPublished - 2014 Jul


  • Interpolation
  • Kernel composition
  • Multiple local linear models
  • Nonlinear separation hyperplane
  • SVM

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics


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