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)

Abstract

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
Pages585-589
Number of pages5
Publication statusPublished - 2013
Externally publishedYes
EventScience and Information Conference, SAI 2013 - London, United Kingdom
Duration: 7 Oct 20139 Oct 2013

Conference

ConferenceScience and Information Conference, SAI 2013
Abbreviated titleSAI 2013
Country/TerritoryUnited Kingdom
CityLondon
Period7/10/139/10/13

Keywords

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

Cite this