원문정보
보안공학연구지원센터(IJFGCN)
International Journal of Future Generation Communication and Networking
Vol.6 No.6
2013.12
pp.25-36
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보