基于SVM和Adaboost解决实时流量识别问题

摘要

针对当前实时流量识别技术上的不足，本文基于支持向量机算法（SVM）和Adaboost算法，提出了一种实时流量识别算法。这种方法将SVM算法使用在Adaboost算法框架中，通过Adaboost算法来提高SVM算法的对流量样本学习能力，改善了SVM算法在实时流量识别中的准确率，从而改善识别器的性能。仿真实验证明，通过设定算法的迭代次数，这种方法的实时流量识别准确率能够达到85%以上。%This paper proposes a real time traffic identification algorithm based on based on support vector machine (SVM) and Adaboost algorithm, to solve the accuracy problem in this ifeld. This algorithm could improve the learning ability of SVM by using Adaboost algorithm, thereby improving the performance of the SVM recognizer in real-time trafifc recognition. Simulation results showed that the accuracy of real-time trafifc identiifcation could be more than 85%by using this algorithm.