Classification on high dimensional data arises in many statistical and data mining studies. Support vector machines (SVM) are one of data mining technique which has been extensively studied and have shown remarkable success in many applications. Many researches developed SVM to increase performance such as smooth support vector machine (SSVM). In this study variants of SSVM (spline SSVM, piecewise polynomial SSVM) are proposed for high-dimensional classification. Theoretical results demonstrate piecewise polynomial SSVM has better classification. And numerical comparison results show that the piecewise polynomial SSVM slightly better performance than spline SSVM.

Original languageEnglish
Pages (from-to)477-484
Number of pages8
JournalProcedia Computer Science
Publication statusPublished - 2015
Event3rd Information Systems International Conference, 2015 - Shenzhen, China
Duration: 16 Apr 201518 Apr 2015


  • SSVM
  • classification
  • high-dimensional data
  • polynomial
  • spline


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