A fast coreset minimum enclosing ball kernel machines

Xunkai Wei, Chun Hung Roberts Law, Lei Zhang, Yue Feng, Yan Dong, Yinghong Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

A fast coreset minimum enclosing ball kernel algorithm was proposed. First, it transfers the kernel methods to a center-constrained minimum enclosing ball problem, and subsequently it trains the kernel methods using the proposed MEB algorithm, and the primal variables of the kernel methods are recovered via KKT conditions. Then, detailed theoretical analysis and rigid proofs of our new algorithm are given. After that, experiments are investigated via using several typical classification datasets from UCI machine learning benchmark datasets. Moreover, performances compared with standard support vector machines are seriously considered. It is concluded that our proposed algorithm owns comparable even superior performances yet with rather fast converging speed in the experiments studied in this paper. Finally, comments about the existing problems and future development directions are discussed.
Original languageEnglish
Title of host publication2008 International Joint Conference on Neural Networks, IJCNN 2008
Pages3366-3373
Number of pages8
DOIs
Publication statusPublished - 24 Nov 2008
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, Hong Kong
Duration: 1 Jun 20088 Jun 2008

Conference

Conference2008 International Joint Conference on Neural Networks, IJCNN 2008
Country/TerritoryHong Kong
CityHong Kong
Period1/06/088/06/08

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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