Earticle

초록

영어

Support vector machine (SVM) has been widely applied to small-sample, non-linear and high-dimensional classifications. Many modified SVM algorithms were put forward in recent years. Some of them focus on SVM feature selection and some focus on SVM classification effectiveness. As different input vectors have significant influence on learning effect of decision boundary, this paper proposes a weighted multi-class support vector machine (WSVM) algorithm. The algorithm gives different weights to features according to the importance of their information. WSVM algorithm establishes decision boundaries based on weights and is used to classify educational resources. Experimental results indicate that the method achieves relatively good classification effectiveness.

목차

Abstract
 1. Introduction
 2. Support Vector Machine (SVM)
 3. Weighted Multi-Class Support Vector Machine (WSVM)
 4. Key Technologies to Text Classification
 5. Experiment Effect and Analysis
 6. Conclusions
 Acknowledgement
 References

키워드

저자

• Wen Ying [ Changsha University of Science & Technology, Huanan Changsha ]
• Li Hao [ Hunan zhongyi communication technology Engineering Co., Ltd., Huanan Changsha ]

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