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learning bayesian networks
相关语句
  bayesian网学习
     Learning structure from incomplete data is one of the difficulties of learning Bayesian networks because of computational complexity.
     从不完备数据中学习网络结构是 Bayesian网学习的难点之一 ,计算复杂度高 ,实现困难 .
短句来源
  “learning bayesian networks”译为未确定词的双语例句
     2. A Markov chains Monte Carlo(MCMC) method for learning Bayesian networks is presented.
     2.给出了Bayes网络学习的Markov链Monte Carlo(MCMC)方法。
短句来源
     A method for learning Bayesian networks from missing data
     一种具有缺失数据的贝叶斯网络结构学习方法
短句来源
     Learning Bayesian networks structure based on prediction ability
     基于预测能力的贝叶斯网络结构学习
短句来源
     Learning Bayesian networks in risk management
     用于风险管理的贝叶斯网络学习
短句来源
     Research of learning Bayesian networks structure
     贝叶斯网络结构学习方法研究
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  相似匹配句对
     The Inference and Learning of Bayesian networks
     贝叶斯网络的推理和学习
     Learning Bayesian Network Structure
     贝叶斯网络结构学习分析
短句来源
     Semisupervised Learning of Bayesian Classification
     半监督学习贝叶斯分类(英文)
短句来源
     On Learning
     论学习
短句来源
     3. The learning of Bayesian Networks.
     3.贝叶斯网络的学习。
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  learning bayesian networks
In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks.
      
We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data.
      
Learning Bayesian networks: The combination of knowledge and statistical data
      
We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data.
      
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
      
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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;...

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。

Learning structure from incomplete data is one of the difficulties of learning Bayesian networks because of computational complexity. In this paper, an evolutionary algorithm combined with expectation is proposed. Fitness function is presented, which based on expectation, converts incomplete data to complete data utilizing current best structure of evolutionary process to reduce computational complexity, ensuring that this algorithm can evolve for good structure. Besides, encoding is given, and genetic...

Learning structure from incomplete data is one of the difficulties of learning Bayesian networks because of computational complexity. In this paper, an evolutionary algorithm combined with expectation is proposed. Fitness function is presented, which based on expectation, converts incomplete data to complete data utilizing current best structure of evolutionary process to reduce computational complexity, ensuring that this algorithm can evolve for good structure. Besides, encoding is given, and genetic operators are designed, which provides guarantee of convergence. Experimental results show that this algorithm can effectively learn Bayesian network structure from incomplete data.

从不完备数据中学习网络结构是 Bayesian网学习的难点之一 ,计算复杂度高 ,实现困难 .针对该问题提出了一种进化算法 .设计了结合数学期望的适应度函数 ,该函数利用进化过程中的最好 Bayesian网把不完备数据转换成完备数据 ,从而大大简化了学习的复杂度 ,并保证算法能够向好的结构不断进化 .此外 ,给出了网络结构的编码方案 ,设计了相应的遗传算子 ,使得该算法能够收敛到全局最优的 Bayesian网结构 .模拟实验结果表明 ,该算法能有效地从不完备数据中学习 .

In this paper the method of constructing a Bayesian network is introduced and it is summarized what is the characteristic of Bayesian network and the content and process of learning Bayesian network,meanwhile the forecasting formula is introduced on discrete Bayesian network.There are three basic steps in learning Bayesian network,first is to determine the set and domain of variables;second is to determine the structure of network;third is to determine the...

In this paper the method of constructing a Bayesian network is introduced and it is summarized what is the characteristic of Bayesian network and the content and process of learning Bayesian network,meanwhile the forecasting formula is introduced on discrete Bayesian network.There are three basic steps in learning Bayesian network,first is to determine the set and domain of variables;second is to determine the structure of network;third is to determine the local probabilistic distribution.Bayesian network is the graphical model of encoding the assertions of qualitative and quantitative relationships between variables and is a very useful tool for joint analysis and forecasting.

通过示例给出了贝叶斯网络的构造方法 ,概括了贝叶斯网络的特点及贝叶斯网络学习的内容与过程 ,同时给出了离散型贝叶斯网络的预测公式 .贝叶斯网络学习主要有三个基本环节 ,其一是确定变量集和变量域 ;其二是确定贝叶斯网络结构 ;其三是确定局部概率分布 .贝叶斯网络是描述变量之间定性与定量依赖关系的图形模式 ,是进行数据联合分析与预测的有力工具 .

 
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