重要的是从安全提供者的属性获取“NamespaceID ”，而不是左侧的浏览器面板中的Authentication叶下的叶的名称。
It is important to get the "Namespace id" from the properties of the security provider and not the name of the leaf under the Authentication leaf in the explorer pane on the left.
贝叶斯网络是数据采掘的一个非常有效的工具，它能够定性和定量地分析属性之间的依赖关系，进行概率推理。
Bayesian network as, a very useful tool in data mining, can provide qualitative and quantitative relationship between attributes and probability inference.
平面目标闭合曲线的傅里叶描述子只描述了目标区域的边界形状，无法反映目标内在属性。
The Fourier describer only describes the boundary shape of the planner target region, and can not reflect feature inside characteristic.
在树的生成过程中，非叶结点相关属性的选择直接决定树的质量。
Obviously selected attribute of a node is important and related to quality of decision-tree.
基于基本粗糙集合理论中属性不精确或部分依赖关系的定义，提出了一种新的选择性受限树型贝叶斯网络分类器。
A variant of TAN using rough sets theory is presented, and their tree classifier structures, which can be thought of as a selective restricted trees Bayesian classifier, are compared.
并且该算法改进了决策树创建叶节点的条件，从而决策树不会用尽所有的候选属性才停止构造，这就消除了没有原始数据造成的影响。
And the algorithm improves the ending condition of building decision tree which don't stop constructing from using all of the attributes. So it has no influence on original data.
一方面，通过对关联规则的挖掘，发现条件属性之间的关联关系，并且利用这种关联关系弱化朴素贝叶斯的独立性假设；
On the one hand, the associated relationship between condition attributes can be found out through association rules mining, in order to weaken the independent assumption.
树扩展型朴素贝叶斯（TAN）分类器放松了朴素贝叶斯的属性独立性假设，是对朴素贝叶斯分类器的有效改进。
TAN(tree augmented Nave Bayes) takes the Nave Bayes classifier and adds edges to it, it is efficient extend of Nave Bayes.
实验结果表明，属性依赖贝叶斯方法有较好的分类性能。
The experimental results show that Bayes attributes-depending classification has a better performance of classification.
通过分析贝叶斯定理的变形公式和属性相关性度量，提出一种基于强属性限定的贝叶斯分类模型SANBC。
On the basis of analyzing a variant of Bayes theorem and the evaluation of condition attribute with correlation, SANBC is proposed.
针对以上问题，本文研究采用关联规则属性约简和贝叶斯网络相结合的电网故障诊断方法。
Therefore, the authors propose a new approach of Augmented Naive Bayesian network based on Association Rules data mining to diagnose faults in power network.
再根据属性值从底层指标开始，依次逐层计算出叶及各枝节点的得分值，从而计算出评估的最后得分值，由此完成计算机伪码算法。
Then, according to attribute value from the bottom index, the scores of leaves and nodes were calculated to adopt the last evaluation score. At last, the fake code algorithm of computer w…
分类阶段从根开始，按照决策树的分类属性逐层往下划分，直到叶节点，获得概念。
Decision Tree Apply Model to Test Data Test Data Start from the root of...
分类阶段从根开始，按照决策树的分类属性逐层往下划分，直到叶节点，获得概念。
Decision Tree Apply Model to Test Data Test Data Start from the root of...
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