针对P2P流媒体流量识别中的类增量学习问题,提出一种基于“一对一”支持向量机多分类器的类增量学习算法CIOOL.充分利用原有多分类器知识,在不打破原有分类器体系的前提下加入新增类样本知识,以构造出新的多分类器.实验结果表明,CIOOL算法能在保证识别精度的同时减少训练时间和内存消耗,是一种解决P2P流媒体流量识别中类增量问题的有效方法.%This paper studies class incremental learning of P2P streaming traffic identification by using one-against-one Support VectorMachine(SVM) multi-classification. A new SVM class incremental learning algorithm--Class Incremental One-against-One Learning(CIOOL) ispresented. CIOOL can adequately use former knowledge to construct a new multi-classifier without training over again. Experimental results indicate that CIOOL can decrease the time of training and memory consuming, and it is an effective algorithm to solve the problem of class incremental learning in P2P streaming traffic identification.
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