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bayes网络
    Research on the Theory of Bayesian Network and It's Application in Object Detection
    Bayes网络理论及其在目标检测中应用研究
    The Integration of Bayesian Network s &Relational Models:Probabilistic Rel ational Models
    Bayes网络与关系模型的集成:概率关系模型
    Research on Learning Bayesian Network and Its Application in Text Detections
    Bayes网络学习及其在文本检测中的应用研究
    Application of Bayesian Network in Predicting the Trend of Foreign Currency Rate
    Bayes网络在汇率趋势预测中的应用
    Application Research and on Subjective Bayesian Network in Pervasive Computing
    主观Bayes网络在普适计算中的应用研究
    Research on Explanation Function for Reason Conclusions with Bayesian Network
    Bayes网络推理结论的解释机制研究
    3. The mixtures of factor analyzers is a Bayesian network with special structures.
    3.研究了两种特殊结构的Bayes网络模型,混合因子分析模型和独立分量分析模型。
    4. A new object-oriented probabilistic graphical model, named object probabilistic model (OPM) is put forward, which can reduce the complexity of knowledge representation and inference of a large-scale Bayesian network.
    4.针对大规模Bayes网络知识表示和推理的计算复杂度问题,提出了一种新的面向对象的概率图模型——对象概率模型(OPM)。
    Bayesian Network relaxes the assumption of independence,but its algorithm is very complex.
    而Bayes网络较好地考虑了属性之间的依赖关系,但是其计算相当复杂。
    In this paper, we applied Bayesian network with plates as analysis model to propose a learning algorithm that can derive private conditional probabilities from grouped sample. We have exploited a two level learning architecture: at first level, we have a set of parameters that express private conditional probabilities, i.e. the Θ ij (l)s;
    本文应用带盘的 Bayes网络作为分析模型 ,对于学习实例数据库为分组样本的关于各组样本私有条件概率的学习算法进行讨论 ,构建出两层学习结构 :第一层针对各组私有条件概率分布 Θij(l) s的学习 ;
    A learning approach is proposed to solve the problems of conditional probability assignation in large scale Bayesian network.
    针对大规模Bayes网络的条件概率赋值问题,提出一种学习方法.
    Firstly, a new hierarchical Bayesian Network model is defined based on class hierarchical structure, which is used to represent large scale Bayesian network.
    首先使用类层次结构定义一种新的层次Bayes网络模型,用于表示大规模Bayes网络.
    Finally, experiments in automatic detection and location of texts in images show the feasibility of this hierarchical Bayesian network and learning approach.
    最后将本层次Bayes网络及计算公式用于解决图像中文本的自动检测与定位问题,实验结果表明了它们的有效性.
    In this paper, an explanation function about Bayesian network is presented. With it, evidences' effect degree, direction and paths on reason conclusion can be explained.
    提出一种关于Bayes网络的解释机制,用于解释证据对推理结论的作用程度、方向及路径.
    Firstly, a new hierarchical Bayesian Network model is defined based on class hierarchical structure,which is used to represent large scale Bayesian networks.
    首先使用类层次结构定义一种新的基于层次的Bayes网络模型,用于表示大规模Bayes网络.
    Experiments in automatic detection and location of texts in images show the feasibility of this hierarchical Bayesian network and learning approach.
    将层次Bayes网络及计算公式用于解决图像中文本的自动检测与定位问题,实验结果表明了它们的有效性.
    The paper makes analysis to the simple and common naive Bayesian categorization model,and presents an approach to improve performance of Bayesian classification model using weight adjustment and an approach to make non-tutor Bayesian categorization using EM algorithm when lacking mass training texts,and discusses how to fix the framework of Bayesian network using heuristic methods so as to make text classification in real circumstance.
    该文对朴素Bayes文本分类器进行了分析,提出了利用权值调整机制改善其分类性能的方法,以及在缺乏大量训练文本的情况下,利用EM算法进行非监督Bayes分类的方法,并讨论了如何运用启发式方法确定Bayes网络结构,在更贴近真实环境的情况下进行文本分类。
    This paper applies Subjective Bayesian Network to infer about context in pervasive environment and proposes SBOWL language,which extended OWL,to make Subjective Bayesian Network fit for infering about context.
    笔者针对此问题把主观Bayes网络模型应用到普适环境中对上下文进行推理,并且对OWL进行了扩展——SBOWL语言,以适应主观Bayes网络模型在上下文推理中的应用。
 

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