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bayesian network structure learning
相关语句
  贝叶斯网络结构学习
     Development and Prospect of Bayesian Network Structure Learning
     贝叶斯网络结构学习的发展与展望
短句来源
     Bayesian Network Structure Learning and Its Applications
     贝叶斯网络结构学习及其应用研究
短句来源
     Research and Implementation of Dependency Analysis Based Bayesian Network Structure Learning and Classifier
     基于依赖分析的贝叶斯网络结构学习和分类器的研究与实现
短句来源
     It is worth of pointing out that the research of Bayesian network structure learning is becoming the mainstream in the field of causal data mining.
     值得一提的是 ,贝叶斯网络结构学习正在成为因果数据挖掘的主流
短句来源
     The Thesis analyses many kinds of Algorithm about Bayesian network structure learning, and then Setting-up a new Algorithm about structure learning Foundation on hydro-electrical simulation system.
     本文在分析了多种贝叶斯网络结构学习算法的基础上,并且根据水电仿真的应用背景,提出了一种根据多专家提供的规则库进行贝叶斯网络结构学习的新算法。
短句来源
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  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
短句来源
  “bayesian network structure learning”译为未确定词的双语例句
     Research state and future trend of Bayesian network structure learning
     贝叶斯网结构学习的研究现状及发展趋势
短句来源
     A Fast Bayesian Network Structure Learning Algorithm
     一种快速的贝叶斯网结构学习算法
短句来源
     Bayesian network structure learning based on model selection is an NP-hard problem.
     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 .
短句来源
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  bayesian network structure learning
We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC).
      
The max-min hill-climbing Bayesian network structure learning algorithm
      
Mutual information-based Bayesian network structure learning algorithm was first applied to the metabolic profile to infer the regulatory network.
      


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...

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 discusses the purposes and methods of Bayesian network structure learning, then proposes a new algorithm for this task. Based on a fully connected potential graph, we enter the expert knowledge and prior knowledge in order to reduce the query space of the structures. By using CI (conditional independence) tests, it can be pruned a fully connected potential graph to a best PG, which is expected to approximate the undirected version of the underlying directed graph. The experimental results...

This paper discusses the purposes and methods of Bayesian network structure learning, then proposes a new algorithm for this task. Based on a fully connected potential graph, we enter the expert knowledge and prior knowledge in order to reduce the query space of the structures. By using CI (conditional independence) tests, it can be pruned a fully connected potential graph to a best PG, which is expected to approximate the undirected version of the underlying directed graph. The experimental results of fault diagnosis in automobile are provided to illustrate the feasibility and efficiency of the new algorithm.

阐述了贝叶斯网络结构学习的内容与方法 ,提出一种基于条件独立性 (CI)测试的启发式算法。从完全潜在图出发 ,融入专家知识和先验常识 ,有效地减少网络结构的搜索空间 ,通过变量之间的CI测试 ,将全连接无向图修剪成最优的潜在图 ,近似于有向无环图的无向版。通过汽车故障诊断实例 ,验证了该算法的可行性与有效性。

From the initial stage of probabilistic Bayesian network construction to the flourishing stage of causal Bayesian network structure learning, this paper firstly reviews Bayesian network structure learning. Then its current problems, related researches and prospects are discussed. It is worth of pointing out that the research of Bayesian network structure learning is becoming the mainstream in the field of causal data mining.

从最初的概率贝叶斯网络构建阶段到涌现大量研究成果的因果贝叶斯网络结构学习阶段 ,本文完整地回顾了贝叶斯网络结构学习的整个发展历程 ,并对该领域当前存在的问题及相关研究进行分析论述 ,给出了研究展望 .值得一提的是 ,贝叶斯网络结构学习正在成为因果数据挖掘的主流

 
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