Earticle

초록

영어

In this paper an adaptive wavelet kernel based on density SVM approach for P2P traffic classification is presented. The model combines the multi-scale learning ability of wavelet kernel and the advantages of support vector machine. Mexican hat wavelet function is used to build SVM kernel function. The wavelet kernel function is tuned adaptively according to the density of samples around support vectors for several times during the training process. The experimental results show that the presented model can improve classification accuracy while reducing the number of support vectors and has better performance for solving P2P traffic classification.

목차

Abstract
 1. Introduction
 2. Basic Concepts of SVM Classification and Wavelet Theory
  2.1. Support Vector Machine
  2.2. Wavelet Kernel Function
 3. Adaptive Wavelet SVM for P2P Classification
  3.1. Adaptive Kernel Function
  3.2. Adaptive SVM Training Algorithm
 4. Experimental Results
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Xinlu Zong [ School of Computer Science and Technology, Hubei University of Technology Wuhan 430068, China ]
• Chunzhi Wang [ School of Computer Science and Technology, Hubei University of Technology Wuhan 430068, China ]
• Hui Xu [ School of Computer Science and Technology, Hubei University of Technology Wuhan 430068, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605