Earticle

초록

영어

In this paper it is proposed a boundary based classifier that is inspired by SVDD and makes an important role for gravity center of training samples. In the proposed method all training samples intervene in determining the classifier boundary. Consequently, the relevant classifier isn’t placed in the group of the support vector machines. Due to the employment of this idea, this method is called "Quasi Support Vector Data Description (QSVDD)". The ability of this method to eliminate the effect of noisy training samples on synthetic data is shown. Experiments on real data sets show that the proposed method describes more accurately lots of real data sets than SVDD.

목차

Abstract
 1. Introduction
 2. Support Vector Data Description
 3. Quasi Support Vector Data Description (QSVDD).
 4. Mathematical Discussion for QSVDD Method
 5. Experimental Results and Comparative Analysis
 6. Conclusion and Future Works
 References

키워드

저자

• Yonos Allahyari [ Department of Computer Engineering, Ferdowsi University of Mashhad ]
• Hadi Sadoghi-Yazdi [ Department of Computer Engineering, Ferdowsi University of Mashhad, Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605