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P2P traffic identification based on bayesian regularization BP neural network

机译:基于贝叶斯正则化BP神经网络的P2P流量识别

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It will increase identification accuracy when P2P traffic identification method based on flow feature is combined with machine learning methods. Recently, the most applied machine learning method is neural networks, but neural networks has insufficient generalization ability, this paper proposes an identification method based on BP neural network that use bayesian regularization to improve its generalization ability. The simulation results show that this method can effectively improve the identification accuracy in practice.
机译:将基于流特征的P2P流量识别方法与机器学习方法结合起来,将提高识别的准确性。近年来,最常用的机器学习方法是神经网络,但神经网络的泛化能力不足,本文提出了一种基于贝叶斯正则化的基于BP神经网络的识别方法,以提高泛化能力。仿真结果表明,该方法在实际中可以有效地提高识别精度。

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