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|  | | 为了更好的帮助您理解掌握查询词或其译词在地道英语中的实际用法,我们为您准备了出自英文原文的大量英语例句,供您参考。 | |
Application of bayesian network learning methods to land resource evaluation
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All these prove the method is feasible and efficient, and indicate that Bayesian network is a promising approach for land resource evaluation.
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Uncertainty modeling based on Bayesian network in ontology mapping
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Research on Bayesian network based user's interest model
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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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This paper presents a new framework on modeling uncertainty in ontologies based on bayesian networks (BN).
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Using Bayesian networks to model promising solutions from the current population of the evolutionary algorithms can ensure efficiency and intelligence search for the optimum.
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Franco Taroni, Colin Aitken, Paolo Garbolino, Alex Biedermann: Bayesian networks and probabilistic inference in forensic science
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Assessing the foundation for Bayesian networks: a challenge to the principles and the practice
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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 ineffi... | | 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。 | | 文摘来源 | | 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。 | | 文摘来源 | | 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, w... | | 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。 | | 文摘来源 | |   | | << 更多相关文摘 |
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