一種基于改進隱馬爾可夫的多媒體業(yè)務分類算法
doi: 10.11999/JEIT140340
基金項目:
國家自然科學基金(61401004, 61271233, 60972038, 61101105)，教育部高等學校博士學科點專項科研基金(20103223110001, 20113223120001)，工業(yè)與信息化部通信軟科學課題(2011-R-70), 2011年度江蘇省研究生培養(yǎng)創(chuàng)新工程(CXZZ11_0396)和安徽師范大學科研培育基金(2013xmpy10)資助課題
A Multimedia Traffic Classification Method Based on Improved Hidden Markov Model
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摘要: 該文提出一種基于改進隱馬爾可夫(Hidden Markov Model, HMM)的多媒體業(yè)務分類算法。改進后的算法保持典型HMM模型結構不變，通過區(qū)分包大小的位置信息，改變發(fā)射概率取值，提高了多媒體業(yè)務區(qū)分性能。理論分析表明，該文模型在計算量上低于高階HMM；實驗結果表明，改進的HMM多媒體業(yè)務分類算法的區(qū)分效果優(yōu)于現(xiàn)有的HMM多媒體業(yè)務分類方法。
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關鍵詞:
- 多媒體業(yè)務流識別 /
- 隱馬爾可夫 /
- 發(fā)射概率 /
- 包大小
Abstract: This paper proposes an improved Hidden Markov Model (HMM) based multimedia traffic classification method. This method preserves the classical HMM model structure, and improves the performance of multimedia traffic classification by changing the emitting probability value with the position information of packet size. Theoretical analysis indicates that the new model can reduce the computational complexity of the classical HMM model. Simulation results show that the proposed method can improve the classification performance compared with the existing HMM based classification method. -
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