基于加权观测的隐马尔可夫模型

doi:10.16451/j.cnki.issn1003-6059.201906004

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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 words： Activity Recognition Hidden Markov Model Baum-Welch Algorithm Sequence Labeling

Received: 28 December 2018

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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
	LI Zhehui
	WANG Bo
	XU Yuwei
	HUANG Wanwei

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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