Earticle

초록

영어

Twin support vector machine (TWSVM) was initially designed for binary classification. However, real-world problems often require the discrimination more than two categories. To tackle multi-class classification problem, in this paper, a multiple least squares twin support vector machine is proposed. Our Multi-LSTSVM solves K quadratic programming problems (QPPs) to obtain K hyperplanes, each problem is similar to binary LSTSVM. Comparison against the Multi-LSSVM, Multi-GEPSVM, Multi-TWSVM and our Multi-LSTSVM on both UCI datasets and ORL, YALE face datasets illustrate the effectiveness of the proposed method.

목차

Abstract
 1. Introduction
 2. Background
  2.1. Least Squares Support Vector Machine (LSSVM)
  2.2. Least Squares Twin Support Vector Machine (LSTSVM)
 3. Multi-LSTSVM
  3.1. Linear Multi-LSTSVM
  3.2. Nonlinear Multi-LSTSVM
 4. Experimental Results
  4.1. UCI Datasets
  4.2. Image Classification
 5. Conclusions
 Acknowledgments
 References

키워드

저자

• Sugen Chen [ School of Mathematics & Computational Science, Anqing Normal University, Anqing Anhui, 246133, PR China ]
• Juan Xu [ School of Mathematics & Computational Science, Anqing Normal University, Anqing Anhui, 246133, PR China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605