Earticle

초록

영어

Support vector machine is an effective pattern classification method. Its time complexity and space complexity is O(n3) and O(n2) respectively for training samples with scale of n. Meanwhile, for core vector machine, the relation between time complexity and the scale of training samples is linear and space complexity is independent with the scale of training samples. In this paper, for the problem of big data classification, the concept and principle of support vector machine are described and support vector machine is converted into the form of minimum enclosing ball, consequently core vector machine is used to efficiently obtain the approximate optimal solution. Experiments confirm that core vector machine algorithm can classify the big data quickly and efficiently.

목차

Abstract
 1. Introduction
 2. Principle of SVM
  2.1. The Linear Case
  2.2. The Nonlinear Case
 3. Principle of MEB
  3.1. MEB in Computational Geometric
  3.2. The Theory and Kernel Method of MEB
  3.3. The Relationship between SVM and MEB
 4. CVM Algorithm
  4.1. The Step of CVM Algorithm
  4.2. The Detailed Process of CVM Algorithm
 5. Experiments and Analysis
  5.1. Experiment Settings
  5.2. Experiment and Result Analysis
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Dong Ai-mei [ QiLu University of Technology, JiangNan University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605