An SMO Approach to fast SVM for classification of large scale data

Juanxi Lin, Mengnan Song, Jinglu Hu

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

Abstract

In this paper, a novel approach is proposed as a new fast Support Vector Machines (SVM) basing on sequential minimal optimization(SMO), minimum enclosing ball(MEB) approach and active set strategy. The combination with these 3 techniques largely accelerates the training process of SVM, attains fewer support vectors(SVs) as well as obtains a acceptable accuracy comparing to original SVM. From simulation results, it is stated that the proposed method will be a good alternative for classification of large scale data.

Original languageEnglish
Title of host publication2014 International Conference on IT Convergence and Security, ICITCS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479965410
DOIs
Publication statusPublished - 2014 Jan 23
Event4th 2014 International Conference on IT Convergence and Security, ICITCS 2014 - Beijing, China
Duration: 2014 Oct 282014 Oct 30

Publication series

Name2014 International Conference on IT Convergence and Security, ICITCS 2014

Other

Other4th 2014 International Conference on IT Convergence and Security, ICITCS 2014
Country/TerritoryChina
CityBeijing
Period14/10/2814/10/30

Keywords

  • Support Vector Machine
  • active set
  • minimum enclosing ball
  • sequential minimal optimization

ASJC Scopus subject areas

  • Computer Networks and Communications

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