An efficient classification using support vector machines

Ning Ruan, Yi Chen, David Yang Gao

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

2 Citations (Scopus)


Support vector machine (SVM) is a popular method for classification in data mining. The canonical duality theory provides a unified analytic solution to a wide range of discrete and continuous problems in global optimization. This paper presents a canonical duality approach for solving support vector machine problem. It is shown that by the canonical duality, these nonconvex and integer optimization problems are equivalent to a unified concave maximization problem over a convex set and hence can be solved efficiently by existing optimization techniques.

Original languageEnglish
Title of host publicationProceedings of 2013 Science and Information Conference, SAI 2013
Number of pages5
Publication statusPublished - 2013
Externally publishedYes
EventScience and Information Conference, SAI 2013 - London, United Kingdom
Duration: 7 Oct 20139 Oct 2013


ConferenceScience and Information Conference, SAI 2013
Abbreviated titleSAI 2013
Country/TerritoryUnited Kingdom


  • canonical duality
  • classification
  • data mining
  • global optimization
  • support vector machine

Cite this