A Bayesian approach for structural learning with hidden Markov models
A Bayesian approach for structural learning with hidden Markov models
复制标题
使用隐马尔可夫模型进行结构学习的贝叶斯方法
DOI：
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发表时间：
2002
影响因子：
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通讯作者：
Gautam Biswas
中科院分区：
文献类型：
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作者：
Cen Li;Gautam Biswas
隐马尔可夫模型（HMM）已被证明是一个成功的建模范例的动态和空间过程中的许多领域，如语音识别，基因组学，和一般的序列比对。典型地，在这些应用中，模型结构由领域专家预定义。因此，HMM学习问题的重点是学习模型的参数值，以适应给定的数据序列。然而，当人们考虑其他领域，如经济学和生理学，模型结构捕捉系统的动态行为是不可用的。为了在这些领域中成功地应用HMM方法，重要的是要有一种机制可以从数据中自动导出模型结构。本文提出了一种隐马尔可夫模型的学习过程，同时学习的模型结构和最大似然参数值的隐马尔可夫模型从数据。HMM模型的结构推导出基于贝叶斯模型选择方法。此外，我们引入了一个新的初始化过程的HMM参数值估计的基础上的K-均值聚类方法。人工生成数据的实验结果表明了该方法的有效性。
Hidden Markov Models(HMM) have proved to be a successful modeling paradigm for dynamic and spatial processes in many domains, such as speech recognition, genomics, and general sequence alignment. Typically, in these applications, the model structures are predefined by domain experts. Therefore, the HMM learning problem focuses on the learning of the parameter values of the model to fit the given data sequences. However, when one considers other domains, such as, economics and physiology, model structure capturing the system dynamic behavior is not available. In order to successfully apply the HMM methodology in these domains, it is important that a mechanism is available for automatically deriving the model structure from the data. This paper presents a HMM learning procedure that simultaneously learns the model structure and the maximum likelihood parameter values of a HMM from data. The HMM model structures are derived based on the Bayesian model selection methodology. In addition, we introduce a new initialization procedure for HMM parameter value estimation based on the K-means clustering method. Experimental results with artificially generated data show the effectiveness of the approach.