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bayesian网络
相关语句
  bayesian network
     Learning Bayesian Network Structures Based on MDL and Hybrid Genetic Algorithms
     基于MDL原理与混合遗传算法的Bayesian网络结构学习
短句来源
     Modeling E-commerce website quality management based on Bayesian network
     基于Bayesian网络的电子商务网站质量管理模型
短句来源
     Algorithm of Bayesian network structural learning based on information theory
     基于信息论的Bayesian网络结构学习算法研究
短句来源
     Study of Two-layer Trust Model in Grid Based on Bayesian Network
     网格环境下基于Bayesian网络的信任模型研究
短句来源
     An New Intrusion Detection Method Based on ICA Model and Naive Bayesian Network
     一种基于独立分量分析和Naive Bayesian网络的入侵检测方法
短句来源
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  bayesian networks
     This paper discusses the structure and the construction of Bayesian networks, emphasizing the basic methods for learning the structure and probabilities of Bayesian networks from prior knowledge and sample data.
     通过剖析 Bayesian网络的结构和建造步骤 ,着重讨论用 Bayesian方法从先验信息和样本数据进行学习以确定网络的结构和概率分布的基本方法 ,分析 Bayesian网络学习的特点 ,探讨 Bayesian网络的适用性。
短句来源
     AN IMPROVED BAYESIAN NETWORKS LEARNING ALGORITHM
     一种改进的Bayesian网络结构学习算法
短句来源
     One of the difficulties of the application of Bayesian Networks is that when the data arise, it is very hard to learn the structures of Bayesian Networks from large databases.
     从大型数据库中学习Bayesian网络结构是Bayesian网络应用的难点之一。
短句来源
     Handling the incomplete data problem using Bayesian networks
     用Bayesian网络处理具有不完整数据的问题分析
短句来源
     Dealing with Incomplete Data Based on Bayesian Networks
     基于Bayesian网络的缺损数据处理方法
短句来源
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  “bayesian网络”译为未确定词的双语例句
     ACOB(ant colony optimization B algorithm) is an algorithm of the metaheuristic to solve the problem.
     ACOB算法(蚁群优化B算法)是其中一种基于元启发引入蚂蚁机制来进行Bayesian网络学习的方法.
短句来源
     Building macroeconomic system models with DBNs
     用动态Bayesian网络建立宏观经济系统模型
短句来源
     Distance inquiry system based on Bayesian net theory
     基于Bayesian网络理论的远程查询系统
短句来源
     Bayesian network-based multiple granularity trust model in P2P
     P2P环境下基于Bayesian网络的多粒度信任模型
短句来源
     Spam filtering algorithm based on supervised Bayesian parameter estimation
     基于有监督Bayesian网络的垃圾邮件过滤
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  bayesian network
Application of bayesian network learning methods to land resource evaluation
      
All these prove the method is feasible and efficient, and indicate that Bayesian network is a promising approach for land resource evaluation.
      
Uncertainty modeling based on Bayesian network in ontology mapping
      
Research on Bayesian network based user's interest model
      
On the basis of analyzing the existing users' interest models and some basic questions of users' interest (representation, derivation and identification of users' interest), a Bayesian network based users' interest model is given.
      
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  bayesian networks
This paper presents a new framework on modeling uncertainty in ontologies based on bayesian networks (BN).
      
Using Bayesian networks to model promising solutions from the current population of the evolutionary algorithms can ensure efficiency and intelligence search for the optimum.
      
Franco Taroni, Colin Aitken, Paolo Garbolino, Alex Biedermann: Bayesian networks and probabilistic inference in forensic science
      
Assessing the foundation for Bayesian networks: a challenge to the principles and the practice
      
Learning Bayesian networks using various datasources and applications to financial analysis
      
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Much of the current research in learning Bayesian networks fails to effectively deal with missing data. This paper presents two methods to account for missing data. One method recasts the incomplete data set into a complete data set and then learns Bayesian networks from the complete data set. The other learns Bayesian networks directly from the incomplete data set and this method is gradually correct. The experimental results show that the former provides accurate results, but is inefficient; while the latter...

Much of the current research in learning Bayesian networks fails to effectively deal with missing data. This paper presents two methods to account for missing data. One method recasts the incomplete data set into a complete data set and then learns Bayesian networks from the complete data set. The other learns Bayesian networks directly from the incomplete data set and this method is gradually correct. The experimental results show that the former provides accurate results, but is inefficient; while the latter is highly efficient, and can obtain good results when the data set is large. Furthermore, both methods perform better than other methods that deal with missing data.

针对现有的 Bayesian网络学习方法都不能有效处理缺失数据问题 ,论文给出了两种处理不完整数据问题的方法 :一种方法是先把不完整的数据集修复成完整的数据集 ,利用完整的数据集进行计算 ,并将结果作为不完整数据集对应情况的近似 ;另一种方法是直接使用不完整的数据集进行近似计算 ,而这种近似计算是渐进正确的。实验结果表明前一种方法计算结果准确 ,但效率较低 ;后一种方法效率较高 ,在数据量比较大时能达到很好的效果 ;而且这两种方法的性能比其它处理缺失数据的方法效果要好。

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. Virtually, it is testified to be effective and practical by putting this method into actual examples, where choosing...

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. Virtually, it is testified to be effective and practical by putting this method into actual examples, where choosing tree seeds in virescence decision making is considered.

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。通过将该方法应用于实际问题中的例子:绿化决策系统中如何选取树种,我们将看到此技术是有效和实用的。

 
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