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bayesian network classifier
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  贝叶斯网络分类器
     To overcome the defect that K2 algorithm requires the suitable order of nodes in advance while dealing with the structure learning of Bayesian Network Classifier (BNC), the algorithm GA-K2 is proposed which introduces the integer coding genetic algorithm based on selective ensemble concept to K2. It provides the guarantee of getting the best order of nodes and the convergence of Bayesian network structure for K2 in global optimization.
     为克服K2算法在处理贝叶斯网络分类器(BayesianNetworkClassifier,BNC)结构学习中要求先指定适合节点次序的缺点,提出GA-K2算法,将基于选择性集成的整数编码遗传算法引入到K2算法中,使之能得到最佳节点次序并且网络结构收敛到全局最优.
短句来源
     Novel Method for Transformer Faults Integrated Diagnosis Based on Bayesian Network Classifier
     基于贝叶斯网络分类器的变压器综合故障诊断方法
短句来源
     It introduces the building of experiment platform MBNC for Bayesian Classifiers using Matlab based on BNT, including the system structure and the main function of MBNC, the Classifiers built on MBNC: the Nave Bayesian Classifier NBC, the Tree Augmented Nave Bayesian Classifier TANC based on Mutual Information and Conditional Mutual Information, Bayesian Network Classifier BNC based on K2 and GS algorithm.
     介绍了用Matlab在BNT软件包基础上建构的贝叶斯分类器实验平台MBNC,阐述了MBNC的系统结构和主要功能,以及在MBNC上建立的朴素贝叶斯分类器NBC,基于互信息和条件互信息测度的树扩展的贝叶斯分类器TANC,基于K2算法和GS算法的贝叶斯网络分类器BNC.
短句来源
     BAN,i. e. BN augmented Nave-Bayes,is an augmented Bayesian network classifier,whose accuracy is easy to improve by the Boosting technique.
     BAN(BN augmented Nave-Bayes)是一种增强的贝叶斯网络分类器,通过提升很容易提高其分类性能.
短句来源
     AN ACTIVE BAYESIAN NETWORK CLASSIFIER
     主动贝叶斯网络分类器
短句来源
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  贝叶斯分类器
     It introduces the building of experiment platform MBNC for Bayesian Classifiers using Matlab based on BNT, including the system structure and the main function of MBNC, the Classifiers built on MBNC: the Nave Bayesian Classifier NBC, the Tree Augmented Nave Bayesian Classifier TANC based on Mutual Information and Conditional Mutual Information, Bayesian Network Classifier BNC based on K2 and GS algorithm.
     介绍了用Matlab在BNT软件包基础上建构的贝叶斯分类器实验平台MBNC,阐述了MBNC的系统结构和主要功能,以及在MBNC上建立的朴素贝叶斯分类器NBC,基于互信息和条件互信息测度的树扩展的贝叶斯分类器TANC,基于K2算法和GS算法的贝叶斯网络分类器BNC.
短句来源
     1. In our research, the Bayesian network classifier is firstly used in the transformer fault diagnosis. We develops three classifier models: NB, TAN and BAN classifier, which hold the high accuracy when there is not much information lost for Bayesian classifier is able to handle incomplete information, in transformer fault diagnosis.
     1. 首次将贝叶斯网络分类器应用到变压器故障诊断领域,提出了用于变压器故障诊断的NB、TAN 和BAN 三种贝叶斯分类器模型,这些模型因贝叶斯分类器具有处理不完备信息的能力,在信息缺失不多时仍然具有较高的正判率。
短句来源
     Secondly we introduce a few Bayesian classification models, such as Naive Bayesian Classifier, Bayesian Network Classifier and Incremental Bayesian Classifier.
     接下来主要介绍了几种贝叶斯分类模型:朴素贝叶斯分类器,贝叶斯网络分类器,增量贝叶斯分类模型等,并对其特点进行分析。
短句来源
  “bayesian network classifier”译为未确定词的双语例句
     Bayesian Network Classifier and Its Application in CRM
     基于贝叶斯网的分类器及其在CRM中的应用
短句来源
     Bayesian network classifier has good capability to handle problems with high uncertainty, and is fit for customer modeling in CRM.
     基于贝叶斯网的分类器因其对不确定性问题有较强的处理能力,因此在CRM客户建模中有其独特的优势。
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  bayesian network classifier
The experiments show that the selective unrestricted Bayesian network classifier outperforms the na?ve Bayes and the tree-augmented na?ve Bayes decision rules concerning the classification rate.
      
The experiments show that the selective unrestricted Bayesian network classifier outperforms the na?ve Bayes and the tree-augmented na?ve Bayes decision rules concerning the classification rate.
      
In this paper we present the Dempster-Shafer theory as a framework within which the results of a Bayesian network classifier and a fuzzy logic-based classifier are combined to produce a better final classification.
      
The simplest restricted Bayesian network classifier is the naive Bayesian classifier.
      
The graph visualization option only appears if a Bayesian network classifier has been built.
      
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Bayesian network classifier has good capability to handle problems with high uncertainty, and is fit for customer modeling in CRM. Based on the analysis of naive Bayesian classifier and general Bayesian network classifier, augmented naive Bayesian classifier and Bayesian multi-net classifier were introduced, and the detailed algorithm of the latter one was described. We applied Bayesian multi-net classifier in customer modeling of telecommunications...

Bayesian network classifier has good capability to handle problems with high uncertainty, and is fit for customer modeling in CRM. Based on the analysis of naive Bayesian classifier and general Bayesian network classifier, augmented naive Bayesian classifier and Bayesian multi-net classifier were introduced, and the detailed algorithm of the latter one was described. We applied Bayesian multi-net classifier in customer modeling of telecommunications CRM, and got effective results.

基于贝叶斯网的分类器因其对不确定性问题有较强的处理能力,因此在CRM客户建模中有其独特的优势。在对朴素贝叶斯分类器、通用贝叶斯网分类器优缺点分析的基础上,引入增强型BN分类器和贝叶斯多网分类器,详细介绍了后者的算法,并将其应用到实际电信CRM客户建模中,取得较好的效果。

The classification is an important and basic ability for human obtained by learning. It has been considered as a key research area in machine learning, pattern recognition and data mining. It is proved that a Bayesian network classifier restricted by class variable is optimal under zero-one loss rate. The most important problem of setting up the classifier is to learning the structure of attributes Bayesian network restricted by class variable. In this paper, the method of learning the...

The classification is an important and basic ability for human obtained by learning. It has been considered as a key research area in machine learning, pattern recognition and data mining. It is proved that a Bayesian network classifier restricted by class variable is optimal under zero-one loss rate. The most important problem of setting up the classifier is to learning the structure of attributes Bayesian network restricted by class variable. In this paper, the method of learning the structure of attributes Bayesian network is developed. In learning the method of orienting edges based on the causal semanitics of an arc's direction is used. The method is combined with that of orienting edges based on collider identification to make superfluous arcs disposal after orienting edges. The problems brought by checking superfluous edges before orienting edges are avoided. The efficiency and veracity of learning Bayesian network structure is markedly improved. A contrast experiment is conducted by simulation and the results are analyzed.

分类能力是人类经过学习得到的重要而基本的能力 ,也是机器学习、模式识别和数据采掘研究的核心问题 .在0 - 1损失率下 ,证明了基于类约束的贝叶斯网络分类器是最优分类器 .建立该分类器的核心问题是基于类约束属性贝叶斯网络结构学习 ,给出了学习属性贝叶斯网络结构的方法 ,在学习过程中使用了根据弧方向因果语义确定边方向的方法 ,并和碰撞识别定向相结合 ,在边定向之后进行冗余弧检验 ,解决了目前冗余边检验在定向之前所导致的问题 ,显著提高了结构学习效率和准确性 .并使用模拟数据进行了分类实验和分析 .

To test and evaluate the performance of Bayesian Classifier, it is absolutely necessary to carry through contrastive experiment using different data sets. Current packages for Bayesian Classifier experiment are designed for certain purposes, so that it can't satisfy the needs of different research. It introduces the building of experiment platform MBNC for Bayesian Classifiers using Matlab based on BNT, including the system structure and the main function of MBNC, the Classifiers...

To test and evaluate the performance of Bayesian Classifier, it is absolutely necessary to carry through contrastive experiment using different data sets. Current packages for Bayesian Classifier experiment are designed for certain purposes, so that it can't satisfy the needs of different research. It introduces the building of experiment platform MBNC for Bayesian Classifiers using Matlab based on BNT, including the system structure and the main function of MBNC, the Classifiers built on MBNC: the Nave Bayesian Classifier NBC, the Tree Augmented Nave Bayesian Classifier TANC based on Mutual Information and Conditional Mutual Information, Bayesian Network Classifier BNC based on K2 and GS algorithm. MBNC is tested by standard data set from UCI and the results show that the performance of Bayesian classifiers built on MBNC are preceded similar works and the quantity of programming much less than that using current packages, which indicates that the platform works correctly, effectively and stably. Now the experiments for optimizing Bayesian Classifiers and the study of dealing with missing data are carried through on MBNC.

为了测试评估贝叶斯分类器的性能,用不同数据集进行对比实验是必不可少的.现有的贝叶斯网络实验软件包都是针对特定目的设计的,不能满足不同研究的需要.介绍了用Matlab在BNT软件包基础上建构的贝叶斯分类器实验平台MBNC,阐述了MBNC的系统结构和主要功能,以及在MBNC上建立的朴素贝叶斯分类器NBC,基于互信息和条件互信息测度的树扩展的贝叶斯分类器TANC,基于K2算法和GS算法的贝叶斯网络分类器BNC.用来自UCI的标准数据集对MBNC进行测试,实验结果表明基于MBNC所建构的贝叶斯分类器的性能优于国外同类工作的结果,编程量大大小于使用同类的实验软件包,所建立的MBNC实验平台工作正确、有效、稳定.在MBNC上已经进行贝叶斯分类器的优化和改进实验,以及处理缺失数据等研究工作.

 
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