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| 基于加权观测的隐马尔可夫模型 | |
| 引用本文： | 王昌海,李哲辉,王博,许昱玮,黄万伟.基于加权观测的隐马尔可夫模型[J].模式识别与人工智能,2019,32(6):515-523. |
| 作者姓名： | 王昌海 李哲辉 王博 许昱玮 黄万伟 |
| 作者单位： | 1.郑州轻工业大学 软件学院 郑州 450002; 2.河南省科学技术信息研究院 郑州 450003; 3.东南大学 网络空间安全学院 南京 211189 |
| 基金项目： | 国家自然科学基金项目(No.61872439,61702288)、河南省重点研发与推广专项(科技攻关)(No.192102210291,192102210294)、河南省高等学校重点科研项目(No.19A520043)资助 |
| 摘 要： | 针对隐马尔可夫模型无法融合分类结果权值的问题,文中提出加权观测隐马尔可夫模型(WOHMM),并给出模型中概率计算、参数学习、序列标注三个基本问题的解决算法.使用公开数据集对参数学习和序列标注问题进行仿真实验,结果表明,WOHMM 的参数学习算法能得到更接近真实值的模型参数,序列标注算法的效果较优。 |
| 关 键 词： | 动作识别 隐马尔可夫模型 Baum-Welch 算法 序列标注 |
| 收稿时间： | 2018-12-28 |
An Improved Hidden Markov Model Based on Weighted Observation |
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| WANG Changhai,LI Zhehui,WANG Bo,XU Yuwei,HUANG Wanwei.An Improved Hidden Markov Model Based on Weighted Observation[J].Pattern Recognition and Artificial Intelligence,2019,32(6):515-523. | |
| Authors: | WANG Changhai LI Zhehui WANG Bo XU Yuwei HUANG Wanwei |
| Affiliation: | 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 |
| 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. |
| Keywords: | Activity Recognition Hidden Markov Model Baum-Welch Algorithm Sequence Labeling |
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