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bayesian networks structure learning
相关语句
  贝叶斯网络结构学习
     Algorithm for Bayesian Networks Structure Learning Based on Information Entropy
     基于约束最大信息熵的贝叶斯网络结构学习算法
短句来源
     RESEARCH ON BAYESIAN NETWORKS STRUCTURE LEARNING BASED ON ROUGH SET
     基于Rough Set的贝叶斯网络结构学习研究
短句来源
     The entropy is based on KL distance, mutual information and maximum mutual information. A hill-climb algorithm is used in Bayesian networks structure learning.
     本文以KL距离、相互信息以及最大相互信息为基础,通过附加合适的约束函数降低变量维数和网络结构的复杂度,提出了一种附加约束的最大熵记分函数,并结合爬山法设计一种贝叶斯网络结构学习的启发式算法.
短句来源
  相似匹配句对
     Research of learning Bayesian networks structure
     贝叶斯网络结构学习方法研究
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
     Research on Learning Bayesian Networks Structure with Missing Data
     具有丢失数据的贝叶斯网络结构学习研究
短句来源
     Structure Learning of Bayesian Networks and Its Applications in Data Mining
     贝叶斯网络结构学习及其在数据挖掘中的应用研究
短句来源
     This paper discusses the structure learning for Bayesian networks.
     提出了用于结构学习的一种新的附加约束的最大相互信息记分函数(MMI-R)。
短句来源
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  bayesian networks structure learning
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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 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...

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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