[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5351/KJAS.2004.17.1.075

Improving the Generalization Error Bound using Total margin in Support Vector Machines

Yoon, Min (Dept. of Applied Statistics, Yonsei University)

Publication Information

The Korean Journal of Applied Statistics / v.17, no.1, 2004 , pp. 75-88 More about this Journal

Abstract

The Support Vector Machine(SVM) algorithm has paid attention on maximizing the shortest distance between sample points and discrimination hyperplane. This paper suggests the total margin algorithm which considers the distance between all data points and the separating hyperplane. The method extends existing support vector machine algorithm. In addition, this newly proposed method improves the generalization error bound. Numerical experiments show that the total margin algorithm provides good performance, comparing with the previous methods.

Keywords

Soft Margin; Total Margin; Support Vector Machines(SVMs); Generalization Error Bound; Soft Margin; Surplus Variables;

Citations & Related Records

Times Cited By KSCI : 1 (Citation Analysis)

Reference
Cited By KSCI

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KSCI

Improving the Generalization Error Bound using Total margin in Support Vector Machines 서포트 벡터 기계에서 TOTAL MARGIN을 이용한 일반화 오차 경계의 개선

Improving the Generalization Error Bound using Total margin in Support Vector Machines