An Improved Method for Multi-class Support Vector Machines

Based on analyzing the advantages and disadvantages of existing multi-class support vector machines, we construct an improved multi-class support vector machines based on binary tree structure, adopting a new metrics to determine the classification order which determines each sub-classifier and its location, the new metrics synthesizes mixed degree and distance between classes. Then we do a measuring experiment using the improved multi-class support vector machines, which identifies five major P2P IPTV applications, the results show that our method is better than one-against-all and one-against-one method.