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Stump Network text classifier is compared with naive bayes text classifier and TAN(tree augmented naive bayes) by an experiment. 将该方法与朴素贝叶斯文本分类器和TAN(tree augmented naive bayes)文本分类器进行实验比较。
tree augmented Naive Bayes classifier 树增广朴素贝叶斯分类
mixed tree augmented naive bayes classifier 混合的树增广朴素贝叶斯分类
TAN (Tree Augmented Naive Bayes) classifier TAN分类器
tree augmented naive bayes 树扩张型朴素贝叶斯
Tree Augmented Naive Bayesian Classifier (TAN) 树增强朴素贝叶斯分类器（TAN）
tree augmented naive Bayesian classifier 树增强朴素贝叶斯分类器
tree augmented naive Bayesian 树增强贝叶斯分类模型
For more information, see Microsoft Naive Bayes Algorithm. 有关详细信息,请参阅Microsoft Naive Bayes算法。
A comparison of event models for Naive Bayes text classification. 如果在监督环境下,即文本的类别已知。
Improving the Performance of Naive Bayes: A Hybrid Approach. 通过逼近改进朴素贝叶斯性能。
Now there are many methods that has been applied to this field, such as SVM, KNN, Naive Bayes, Decision Tree, etc. 目前已经有许多方法应用到该领域。如支持向量机方法(SVM)、K近邻方法(KNN)、朴素贝叶斯方法(Naive Bayes)、决策树方法(Decision Tree)等等。
For more information, see Viewing a Mining Model with the Microsoft Tree Viewer, and Viewing a Mining Model with the Microsoft Naive Bayes Viewer. 有关详细信息,请参阅使用Microsoft树查看器查看挖掘模型和使用Microsoft Naive Bayes查看器查看挖掘模型。
The method based on Naive Bayes Model (NBM) is 92%, it’s a higher precision. 基于贝叶斯模型（NBM ）的有指导消歧的开放测试正确率最高可达92 %25 ，取得了比较好的效果。
To train a Naive Bayes model based on the targeted mailing data in the AdventureWorksDW database. 并根据AdventureWorksDW数据库中的目标邮件数据来为Naive Bayes模型定型。
To use a continuous column in a Microsoft Naive Bayes model, you must discretize the data in the column. 若要在Microsoft Naive Bayes模型中使用连续列,必须对列中的数据进行离散化处理。
The following example adds a Naive Bayes mining model to the New Mailing mining structure. 以下示例将Naive Bayes挖掘模型添加到New Mailing挖掘结构中。
The following example uses the Microsoft Naive Bayes algorithm to create a new mining model. 以下示例使用Microsoft Naive Bayes算法创建新的挖掘模型。
This viewer displays mining models that are built with the Microsoft Naive Bayes algorithm. 该查看器显示使用Microsoft Naive Bayes算法生成的挖掘模型。
To improve efficiency, used naive Bayes classify method to reduce the searching space. 基于效率考虑，利用朴素贝叶斯分类算法减小搜索空间。