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bayesian网
    Research on the Theory of Bayesian Network and Its Application in Image Analysis
    Bayesian网及其在图像分析中的应用研究
    RESEARCH ON LEARNING BAYESIAN NETWORK STRUCTURE BASED ON GENETIC ALGORITHMS
    基于遗传算法的Bayesian网结构学习研究
    LEARNING BAYESIAN NETWORK BY FIRST LEARNING MARKOV NETWORK
    由Markov网到Bayesian网
    Simplification on Bayesian network inference
    Bayesian网推理中的化简方法
    Node Aggregation of Bayesian Network Based on Chain Graph
    基于链图的Bayesian网结点聚集
    Bayesian network in equipment development and its application
    装备研制中的Bayesian网及其应用
    Research on Incremental Learning of Bayesian Network Structure Based on Genetic Algorithms
    基于遗传算法的Bayesian网结构增量学习的研究
    Bayesian Network Optimization Algorithm Based on Holding Strategy
    基于保留策略的Bayesian网优化算法
    Learning Bayesian Network Structure Based on Immune Evolutionary Algorithms
    基于免疫进化算法的Bayesian网结构学习
    Research of a Bayesian Network Structure Learning Algorithm
    一种Bayesian网结构学习算法的研究
    2. Structure learning algorithm of Bayesian network by hybrid genetic algorithm is presented after the drawback of Genetic Algorithm-based learning algorithms is analyzed.
    2.通过分析用遗传算法对Bayesian网进行结构学习时存在的缺点,提出了根据混合遗传算法进行结构学习的新方法:在分析Bayesian网等价结构的基础上提出了Rudimentary结构等价性定理,并将该定理应用于对Bayesian网的结构优化中;
    Parameter learning is main part of learning Bayesian Network models,and it's the basis of Bayesian Network learning.
    参数学习是Bayesian网学习的基础 ,是Bayesian网结构学习必不可少的部分 .
    Because of great difference in constructed model and changes in the dynamics of the domains, it is necessary to improve the performance and accuracy of a Bayesian network as new data is observed.
    已建成的Bayesian网与领域环境间可能存在较大偏差,加之领域本身固有的动态变化特性,因此在观察到新数据时,改善Bayesian网的性能和优化网络结构是十分必要的.
    Rudimentary equivalence theory for structure learning is presented after the equivalence structure of Bayesian network is studied.
    推导了根据现有数据库和网络结构学习Bayesian网参数表的过程;
    And the availability of this new model is verified by analyzing the probability table of the Bayesian network of student scores.
    通过对学生成绩Bayesian网及其变异模型的分析,验证了变异模型的有效性。
    Then the Bayesian network is constructed to fuse these features according to the analysis of the uncertain relationship between them, and experimental results have shown the availability of the solution.
    通过分析各种特征之间的不确定性关系,以及对这些不确定性关系进行处理,建立了文本检测Bayesian网,得到了有效的检测与定位结果。
    The computational complexity of variable elimination is O(n?cw), where n is the number of nodes in a Bayesian network, d is the maximal indegree of nodes, c is the maximal number of assignments of nodes, and w is referred to as width of elimination order.
    变量消去算法的计算复杂性是O(n?cw),其中 n是Bayesian网节点数量,d是 网络中节点的最大入度,c是所有节点(变量)取值的最大数量,w称为消去顺序对应的宽度。
    There exist one or more optimal elimination orders, which makes w minimal, with respect to a given Bayesian network.
    对于一个确定的Bayesian网,存在一个或若干个最优消去顺序,使得其对应的宽度w达到最小值,这个宽度称为树宽度。
    The main content discussed in this paper is about the present status of databases,the method of data mining and it's application in the network establishing of Bayesian: to solve the specific problems of modeling of Bayesian network by means of data mining, i g how to look for the relationship between all the variables from a mass of databases and how to identify the conditional possibility
    本文介绍了数据库技术的现状、数据挖掘的方法以及它在 Bayesian网建网技术中的应用 :通过数据挖掘解决 Bayesian网络建模过程中所遇到的具体问题 ,即如何从大规模数据库中寻找各变量之间的关系以及如何确定条件概率问题。
    The main contents discussed in this paper are about the present status of databases, the method of data - mining and its application in the network establishing of Bayesian: to solve the specific problems of modeling of Bayesian network by means of data - mining, i. e. how to look for the relationship among all variables from a mass of databases and how to identify the conditional possibility.
    本文介绍了数据库技术的现状、数据挖掘的方法以及它在Bayesian网建网技术中的应用:通过数据挖掘解决Bayesian网络建模过程中所遇到的具体问题,即如何从大规模数据库中寻找各变量之间的关系以及如何确定条件概率问题。
 

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