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  2019, Vol. 32 Issue (6): 515-523    DOI: 10.16451/j.cnki.issn1003-6059.201906004
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An Improved Hidden Markov Model Based on Weighted Observation
WANG Changhai1, LI Zhehui2, WANG Bo1, XU Yuwei3, HUANG Wanwei1
1.Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou 450002;
2.Henan Provincial Institute of Scientific and Technical Information, Zhengzhou 450003;
3.School of Cyber Science and Engineering, Southeast University, Nanjing 211189

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Abstract  

As the classic hidden Markov model(HMM) loses the sight of confidence of labeled results while building a sequence, a weighted observation hidden Markov model(WOHMM) is proposed. The algorithms in the steps of probability calculation, parameter learning as well as sequence labeling are described in detail. The simulation results on the public datasets show that the parameters obtained by the parameter learning algorithm of WOHMM are closer to the real values than those of HMM, and the performance of sequence labeling algorithm is superior to the state-of-the-art methods.

Key wordsActivity Recognition      Hidden Markov Model      Baum-Welch Algorithm      Sequence Labeling     
Received: 28 December 2018     
ZTFLH: TP 181  
About author:: (WANG Changhai, Ph.D., lecturer. His research interests include activity recognition, wearable computing and mobile computing.)(LI Zhehui, bachelor, senior engineer. Her research interests include technology of computer applications.)(WANG Bo, Ph.D., lecturer. His research interests include mobile computing, cloud computing, big data platforms and distributed systems.)(XU Yuwei(Corresponding author), Ph.D., associate professor. His research interests include activity recognition, mobile computing and wireless communications.)(HUANG Wanwei, Ph.D., lecturer. His research interests include next-generation network architecture, network security and reconfigurable flexible network.)
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WANG Changhai
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Cite this article:   
WANG Changhai,LI Zhehui,WANG Bo等. An Improved Hidden Markov Model Based on Weighted Observation[J]. , 2019, 32(6): 515-523.
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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201906004      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I6/515
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