DocumentCode :
588934
Title :
An Improved Classification Method of Unstructured P2P Multicast Video Streaming
Author :
Chaobin Liu ; Qiang Guo ; Jie He
Author_Institution :
Inf. Center, Second Mil. Med. Univ., Shanghai, China
Volume :
2
fYear :
2012
fDate :
28-29 Oct. 2012
Firstpage :
415
Lastpage :
418
Abstract :
The classification of unstructured P2P multicast video streaming is the premise for playing online linkage and real-time evidence in the process of network monitoring management. Based on the classification method in the preliminary research, an improved classification method is proposed. the method uses an optimal feature vector extraction algorithm to filter the proposed behavior features in the original method, which greatly lower the feature vector dimension. an improved multi-class support vector machines is also used in the improved method, which not only solves the problem of rejecting sub-regional, but also reduces the computational complexity and improves the identification accuracy. Experiments show that the improved method has less computational and storage overhead, and has higher identification accuracy.
Keywords :
computational complexity; multicast communication; peer-to-peer computing; support vector machines; video streaming; classification method; computational complexity; multiclass support vector machines; network monitoring management; online linkage; optimal feature vector extraction algorithm; real-time evidence; unstructured P2P multicast video streaming; Accuracy; Binary trees; Feature extraction; Streaming media; Support vector machine classification; Vectors; classification; improved method; unstructured P2P multicast video streaming;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4673-2646-9
Type :
conf
DOI :
10.1109/ISCID.2012.259
Filename :
6406027
Link To Document :
بازگشت