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learning bayesian network structure
相关语句
  贝叶斯网络结构学习
     Learning Bayesian network structure with continuous variables based on local optimization
     基于局部优化具有连续变量的贝叶斯网络结构学习
短句来源
     Learning Bayesian Network Structure
     贝叶斯网络结构学习分析
短句来源
     Learning Bayesian Network Structure Based on Nodes Ordering
     基于结点排序的贝叶斯网络结构学习
短句来源
     At present, the method of learning Bayesian network structure with missing data is mainly based on the search and scoring method combined with EM algorithm. The algorithm has low efficiency and easily gets into local optimal structure.
     目前主要基于EM算法和打分-搜索方法进行具有丢失数据的贝叶斯网络结构学习,算法效率较低,而且易于陷入局部最优结构.
短句来源
     In this paper, a new method of learning Bayesian network structure with missing data is presented.
     针对这些问题,建立了一种新的具有丢失数据的贝叶斯网络结构学习方法.
短句来源
更多       
  bayesian网结构学习
     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
短句来源
     Learning Bayesian Network Structure Based on Immune Evolutionary Algorithms
     基于免疫进化算法的Bayesian网结构学习
短句来源
  “learning bayesian network structure”译为未确定词的双语例句
     Improve MDL Principle Used for Learning Bayesian Network Structure
     改进学习贝叶斯网络结构的MDL准则
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  learning bayesian network structure
In section 2.3, we formulated learning Bayesian network structure as an optimization problem in the space of directed acyclic graphs.
      
In section 11.4 we formulated learning Bayesian network structure as an optimization problem in the space of directed acyclic graphs.
      


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,...

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 .

Bayesian network structure learning based on model selection is an NP-hard problem. And none of the presented algorithms is perfectly successful in solving the problem of searching efficiency, especially for complex system learning. Presented in this paper is a new independent-MDL-based approach to learn Bayesian network structures. The proposed algorithm limits the searching space by using a set of lower order independence tests, thus executing the MDL-based...

Bayesian network structure learning based on model selection is an NP-hard problem. And none of the presented algorithms is perfectly successful in solving the problem of searching efficiency, especially for complex system learning. Presented in this paper is a new independent-MDL-based approach to learn Bayesian network structures. The proposed algorithm limits the searching space by using a set of lower order independence tests, thus executing the MDL-based searching algorithm B & B-MDL to obtain the final graph. The precision analysis of algorithm is presented. And the problem of parameter design is also concerned. The result of the experiment shows that the new algorithm I-B & B-MDL is more efficient in time consumption than B & B-MDL algorithm.

基于模型选择的 Bayesian网络 ( BN)结构学习是 NP难的可行解搜索过程 .针对现有算法在复杂系统求解中时间效率低的问题 ,提出了一种新的基于最小描述长度 ( minimal description length)理论的结构学习算法 I-B &B-MDL .这种算法将独立性测度与预测估计相结合 ,在学习过程中引入小计算量的独立性测试为 MDL搜索提供启发性知识 ,限制可行解搜索空间 ,从而加速问题求解过程 .针对新算法讨论了改进策略对求解精度的影响 ,并结合算例分析了独立性测试的阶数选择问题 .通过对一实际问题进行验证表明 ,在保证结果精度的前提下 ,新算法在时间性能上比仅基于预测估计的 B & B-MDL 有较大改进

This paper has extended factor of MD metric by adding a MD attribute which can reflect the difference be-tween destination Network Structure and exist Network Structure,to eliminate the MDL principle's drawback in lacking inherit when used for learning Bayesian Network structure.Improved MDL principle can manage prior knowledge.

文章针对用于BN结构学习的MDL准则在继承性方面的不足,通过扩充DL测度的组成要素,在其中增加一项旨在反映目标网络结构与当前网络结构拓扑差异度的描述长度指标,改进MDL准则,使其具备处理先验知识的能力。

 
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