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bayes网络模型
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  bayesian network model
     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.
短句来源
     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.
短句来源
  “bayes网络模型”译为未确定词的双语例句
     Research on Bayesian Networks and Its Learning Algorithms
     Bayes网络模型及其学习算法研究
短句来源
     Utilizing the conditional independences among a set of random variables, Bayesian networks reduced the number of the parameters needed to encode the joint probability distribution of these variables. At the same time, the use of the graphs to express conditional independences is straightforward and the effective algorithms for the probability inference in Bayesian networks can be obtained by the transformation of the graphs.
     Bayes网络模型利用随机变量之间的条件独立关系,减少了表示一组随机变量的联合概率分布时所需要的参数,并且借助于图的直观表示和变换,我们能够得到在模型中进行概率推理的有效算法。
短句来源
     Some study of Bayesian networks is performed in this dissertation and the work can be summarized as follows:
     本文就Bayes网络模型进行了一些研究,主要工作概括如下:
短句来源
     1. Owing to the complexity of the probability inference in Bayesian networks is NP, some approximating methods are necessary in practice.
     1.由于在一般结构的Bayes网络模型中进行概率推理计算是一个NP问题,因此近似计算是十分必要的。
短句来源
     MCMC can be used to explore the posterior probability produced by the Bayesian learning method.
     当使用Bayes方法学习Bayes网络模型的结构和参数时,使用MCMC方法对后验分布进行抽样研究是非常方便的,它不需要此概率分布是归一化的,而计算归一化常数往往是困难的。
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  相似匹配句对
     The Network Models of Human Relations
     人际关系的网络模型
短句来源
     Perceptron Network Model
     感知机网络模型
短句来源
     An Aerial Image Understanding Model Based on Bayesian Networks
     基于Bayes网络的航空图象理解模型
短句来源
     Research on Bayesian Networks and Its Learning Algorithms
     Bayes网络模型及其学习算法研究
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     Network Data
     网络
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  bayesian networks model
However, Bayesian networks model the entire distribution of each conditional probability and could consume significant computational resources.
      
  bayesian network model
This is the focus of the paper: we propose here to train the structured Bayesian Network model using a cross-entropy training criterion.
      
To compare the alternative approaches in practice, each of the predictive distributions discussed is instantiated in the Bayesian network model family case.
      
A Bayesian Network model of intrusion to be monitored by this agent is provided as an argument to this agent on startup.
      
A dynamic Bayesian network model for autonomous 3d reconstruction from a single indoor image Erick Delage Honglak Lee Andrew Y.
      
A fitness function evaluates the performance of each individual, a Bayesian network model in this case, to measure how close it is to the solution.
      
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As a knowledge representation framework and a kind of probability inference engine, Bayesian networks are widely used in applications for reasoning and decision making with inherent uncertainty. Since the exact algorithms of probability inference in Bayesian networks is NP-hard, as the topology of the network becomes more dense, the run-time complexity of probabilistic inference increases dramatically and real-time decision making eventually becomes prohibitive, so many approximate algorithms based on simulation...

As a knowledge representation framework and a kind of probability inference engine, Bayesian networks are widely used in applications for reasoning and decision making with inherent uncertainty. Since the exact algorithms of probability inference in Bayesian networks is NP-hard, as the topology of the network becomes more dense, the run-time complexity of probabilistic inference increases dramatically and real-time decision making eventually becomes prohibitive, so many approximate algorithms based on simulation or model simplification are proposed. The method discussed in this paper is based on the model simplification of arc removal. In this method, a subset of arcs are selected and removed, which simplifies the network structure and we obtain an approximate network, then any probability inference algorithm can be applied to this approximate network to get a solution within the error bound we predefined. By using the Kullback-Leibler information divergence as the measure of the difference between two probability distributions, this paper discusses the multiple arc removal problem in the gen eral case and presents the optimal parameters for the approximate network. Final ly, a heuristic algorithm is provided which searches a set of arcs to be removed under the upper bound on the probability error allowed.

弧的删除是一种对 Bayes网络模型进行近似的方法 .文中以 Kullback- L eibler偏差作为近似网络和原网络概率分布误差的测度 ,给出了近似网络在此测度意义下的最优参数 .同时 ,也给出了通过对原网络删除多条弧进行近似的启发式算法 ,当给定一个误差上界时 ,可以使用此算法寻找满足误差要求的近似网络

A learning approach is proposed to solve the problems of conditional probability assignation in large scale Bayesian network. Firstly, a new hierarchical Bayesian Network model is defined based on class hierarchical structure, which is used to represent large scale Bayesian network.Then, the train data set is changed from a single table to a database composed of some database tables. And each database table corresponds to a Bayesian network block. Based on that, a formula of conditional probability is developed....

A learning approach is proposed to solve the problems of conditional probability assignation in large scale Bayesian network. Firstly, a new hierarchical Bayesian Network model is defined based on class hierarchical structure, which is used to represent large scale Bayesian network.Then, the train data set is changed from a single table to a database composed of some database tables. And each database table corresponds to a Bayesian network block. Based on that, a formula of conditional probability is developed. And each conditional probabilistic table of Bayesian network block can be calculated from the database tables respectively. Properly adjust the attribute number in each database table can assure the validity of this learning approach. Finally, experiments in automatic detection and location of texts in images show the feasibility of this hierarchical Bayesian network and learning approach.

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.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.然后将训练数据集由单个数据表的形式转化成多表数据库,其中每个数据库表对应1个Bayes网络模块.在此基础上导出条件概率计算公式,从每个数据库表中算出相应的Bayes网络模块的条件概率表,由此实现对整个层次Bayes网络的概率赋值.可通过适当增加数据库表的数目来控制每个表中属性的个数,保证计算的可行性.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.

A learning approach is proposed to solve the problems of conditional probability assignation in large scale Bayesian networks.Firstly, a new hierarchical Bayesian Network model is defined based on class hierarchical structure,which is used to represent large scale Bayesian networks.Then,the train data set is changed from a single table to a database composed of some database tables.And each database table corresponds to a Bayesian network block.Based on that,a formula of conditional probability is developed.And...

A learning approach is proposed to solve the problems of conditional probability assignation in large scale Bayesian networks.Firstly, a new hierarchical Bayesian Network model is defined based on class hierarchical structure,which is used to represent large scale Bayesian networks.Then,the train data set is changed from a single table to a database composed of some database tables.And each database table corresponds to a Bayesian network block.Based on that,a formula of conditional probability is developed.And each conditional probabilistic table of Bayesian network block can be calculated from the database tables respectively.Proper adjustment of the attribute number in each database table can assure the validity of this learning approach.Experiments in automatic detection and location of texts in images show the feasibility of this hierarchical Bayesian network and learning approach.

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.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.然后将训练数据集由单个数据表的形式转化成多表数据库,其中每个数据库表对应一个Bayes网络模块.在此基础上导出条件概率计算公式,从每个数据库表中算出相应的Bayes网络模块的条件概率表,由此实现对整个层次Bayes网络的概率赋值.通过适当增加数据库表的数目来控制每个表中属性的个数,保证计算的可行性.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.

 
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