马尔可夫及隐马尔可夫模型在数据挖掘中的应用

随着用户对于数据挖掘的精确度与准确度要求的日益提高,马尔可夫模型与隐马尔可夫模型被广泛用于数据挖掘领域。本文阐述了马尔可夫模型和隐马尔可夫模型数据挖掘领域的应用,以及隐马尔可夫模型可解决的问题,以供其他研究者借鉴。     

基于隐马尔可夫模型的招聘网络信息抽取

网络信息抽取是从半结构化的Web海量数据中,按用户要求抽取且形成相关的有效的结构数据处理过程。论文以隐马尔科夫模型（HMM）进行数据抽取中的若干关键问题进行研究,提出了基于数据挖掘聚类的模型合并方法生成隐马尔可夫模型,即可根据数据自动生成HMM,同时对一般的隐马尔可夫模型进行了扩展,为每个抽取域生成一个隐马尔可夫模型,用于获取更多的有用信息。     

基于改进粒子群算法的隐马尔可夫模型训练

针对隐马尔可夫模型传统训练算法易收敛于局部极值的问题,提出一种带极值扰动的自适应调整惯性权重和加速系数的粒子群算法,将改进后的粒子群优化算法引入到隐马尔可夫模型的训练中,分别对隐马尔可夫模型的状态数与参数进优化.通过对手写数字识别的实验说明,提出的基于改进粒子群优化算法的隐马尔可夫模型训练算法与传统隐马尔可夫模型训练算法Baum-Welch算法相比,能有效地跳出局部极值,从而使训练后的隐马尔可夫模型具有较高的识别能力.     

隐马尔可夫模型及其在自然语言处理中的应用

基于隐马尔可夫模型(HMM)对汉语文本进行了词性标注,首先介绍隐马尔可夫模型的基本概念,然后着重介绍了隐马尔可夫模型的三个基本问题以及解决问题的基本算法,最后演示了隐马尔可夫模型在词性标注中的简单应用.     

关系马尔可夫网综述

统计关系学习是人工智能领域一个新的研究方向。它通过概率推理模型与逻辑的结合,或概率推理模型与关系模式的结合,来达到更高的预测或分类的准确度。它在机器学习和数据挖掘领域具有广泛的应用前景。详细介绍了一种重要的统计关系模型——关系马尔可夫网的理论模型,并总结关系马尔可夫网当前的研究现状,分析了关系马尔可夫网目前存在的问题以及未来的研究方向。     

隐马尔可夫模型及其最新应用与发展①

隐马尔可夫模型是序列数据处理和统计学习的一种重要概率模型,已被成功应用于许多工程任务中。首先介绍了隐马尔可夫模型的基本原理,接着综述了其在人的行为分析、网络安全和信息抽取中的最新应用。最后对最近提出来的无限状态隐马尔可夫模型的原理及最新发展进行了总结。     

基于自适应隐马尔可夫模型的石油领域文档分词

中文分词技术是把没有分割标志的汉字串转换为符合语言应用特点的词串的过程,是构建石油领域本体的第一步。石油领域的文档有其独有的特点,分词更加困难,目前仍然没有有效的分词算法。通过引入术语集,在隐马尔可夫分词模型的基础上,提出了一种基于自适应隐马尔可夫模型的分词算法。该算法以自适应隐马尔可夫模型为基础,结合领域词典和互信息,以语义约束和词义约束校准分词,实现对石油领域专业术语和组合词的精确识别。通过与中科院的NLPIR汉语分词系统进行对比,证明了所提算法进行分词时的准确率和召回率有显著提高。     

隐马尔可夫模型及其最新应用与发展①

隐马尔可夫模型是序列数据处理和统计学习的一种重要概率模型,已被成功应用于许多工程任务中。首先介绍了隐马尔可夫模型的基本原理,接着综述了其在人的行为分析、网络安全和信息抽取中的最新应用。最后对最近提出来的无限状态隐马尔可夫模型的原理及最新发展进行了总结。     

一种基于隐马尔可夫模型的人脸识别方法

提出一种改进的基于隐马尔可夫模型的人脸识别方法。利用人脸隐马尔可夫模型的结构特征和Viterbi算法的特点,对特征观察序列进行分割,使用部分序列对所有隐马尔可夫模型递进地计算最大相似度,同时排除相似度最小的隐马尔可夫模型,减少观察序列的计算次数,提高识别效率。实验结果表明,该方法能在不降低识别率的情况下,有效提高识别速度。     

隐马尔可夫模型在自然语言理解研究中的应用

自然语言理解是人工智能最活跃的研究领域之一,同时也是目前前沿的课题之一.该领域的研究人员通过对隐马尔可夫模型这一数学模型的跨领域应用,解决了自然语言理解中的瓶颈问题.文章系统阐述了隐马尔可夫模型的原理以及在语音识别和词性标注方面应用的过程,从而为更多研究者了解和认识.     

对隐马尔科夫模型中动态编程的探讨

主要对隐马尔科夫模型中的三个问题（估计问题、译码问题、学习问题）中的译码问题作了研究．并对其应用进行了探讨。     

基于隐马尔可夫模型的音乐分类

音乐类型（Genre）是应用最普遍的管理数字音乐数据库的方式,提出一种基于隐马尔可夫模型（Hidden Markov Models,HMMs）的音乐自动分类方案。在考虑传统的音色特征（Timbre）的同时,将另一重要特征节奏（Tempo）也加以考虑,并通过bagging训练两组HMM进行分类,达到了良好的效果。从结构、状态数和混合高斯模型数三个方面进行了参数优化,找到了最佳的HMM参数。在音乐数据集GTZAN上对传统模型和新模型分类效果进行了测试,结果表明考虑了节奏特征的HMM分类效果更佳。     

Audiovisual integration with Segment Models for tennis video parsing

Automatic video content analysis is an emerging research subject with numerous applications to large video databases and personal video recording systems. The aim of this study is to fuse multimodal information in order to automatically parse the underlying structure of tennis broadcasts. The frame-based observation distributions of Hidden Markov Models are too strict in modeling heterogeneous audiovisual data. We propose instead the use of segmental features, of the framework of Segment Models, to overcome this limitation and extend the synchronization points to the segment boundaries. Considering each segment as a video scene, auditory and visual features collected inside the scene boundaries can thus be sampled and modeled with their native sampling rates and models. Experimental results on a corpus of 15-h tennis video demonstrated a performance superiority of Segment Models with synchronous audiovisual fusion over Hidden Markov Models. Results though with asynchronous fusion are less optimistic.     

A Hidden Markov Models combination framework for handwriting recognition

We propose a general framework to combine multiple sequence classifiers working on different sequence representations of a given input. This framework, based on Multi-Stream Hidden Markov Models (MS-HMMs), allows the combination of multiple HMMs operating on partially asynchronous information streams. This combination may operate at different levels of modeling: from the feature level to the post-processing level. This framework is applied to on-line handwriting word recognition by combining temporal and spatial representation of the signal. Different combination schemes are compared experimentally on isolated character recognition and word recognition tasks, using the UNIPEN international database.Received: 16 August 2002, Accepted: 21 November 2002, Published online: 6 June 2003     

基于隐马尔可夫模型的行为轨迹还原算法

针对行为轨迹还原过程中观察序列状态缺失、无法对终端轨迹进行精确还原的问题,提出一种基于隐马尔可夫模型的行为轨迹还原算法。利用基站布局的空间相关性,在不考虑缺失观察状态的情况下,对隐马尔可夫模型求解过程中的局部概率进行修订,还原出轨迹序列。性能分析和仿真结果表明,状态倾向度越大,轨迹还原成功率越高,当状态倾向度取0.8时,轨迹还原成功率在90%左右。     

Realistic disturbance modeling using Hidden Markov Models: Applications in model-based process control

Understanding and modeling disturbances play a critical part in designing effective advanced model-based control solutions. Existing linear, stationary disturbance models are oftentimes limiting in the face of time-varying characteristics typically witnessed in process industries. These include intermittent drifts, abrupt changes, temporary oscillations, outliers and the likes. This work proposes a Hidden Markov Model-based framework to deal with such situations that exhibit discrete, modal behavior. The usefulness of the proposed disturbance framework – from modeling to ensuring the integral action under a wide variety of scenarios – is demonstrated through several examples.     

A Hidden Markov Model approach for appearance-based 3D object recognition

In this paper, a new appearance-based 3D object classification method is proposed based on the Hidden Markov Model (HMM) approach. Hidden Markov Models are a widely used methodology for sequential data modelling, of growing importance in the last years. In the proposed approach, each view is subdivided in regular, partially overlapped sub-images, and wavelet coefficients are computed for each window. These coefficients are then arranged in a sequential fashion to compose a sequence vector, which is used to train a HMM, paying particular attention to the model selection issue and to the training procedure initialization. A thorough experimental evaluation on a standard database has shown promising results, also in presence of image distortions and occlusions, the latter representing one of the most severe problems of the recognition methods. This analysis suggests that the proposed approach represents an interesting alternative to classic appearance-based methods to 3D object classification.     

A Hybrid Framework for Mobile Robot Localization: Formulation Using Switching State-Space Models

In this paper we address one of the most important issues for autonomous mobile robots, namely their ability to localize themselves safely and reliably within their environments. We propose a probabilistic framework for modelling the robot's state and sensory information based on a Switching State-Space Model. The proposed framework generalizes two of the most successful probabilistic model families currently used for this purpose: the Kalman filter Linear models and the Hidden Markov Models. The proposed model combines the advantages of both models, relaxing at the same time inherent assumptions made individually in each of these existing models.