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- For more information, see Microsoft Naive Bayes Algorithm. 有关详细信息,请参阅Microsoft Naive Bayes算法。
- 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。
- Presently, naive Bayes algorithm for Email filtering has been accepted widely for its simplicity and plainness. 朴素Bayes邮件过滤算法由于简单、易于理解,已被人们广泛接受,并应用到一些商用邮件系统当中。
- The Microsoft Naive Bayes algorithm is a classification algorithm that is quick to build and that works well for predictive modeling. Microsoft Naive Bayes算法是一种可快速构建并适合进行预测性建模的分类算法。
- Naive Bayes Algorithm 朴素贝叶斯算法
- 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。
- This paper uses the improved K-means (IKM) algorithm to process the missing data and thus improve the precision of the Naive Bayes classifier. 本文利用改进的K-均值算法对缺失数据进行处理，提高了朴素贝叶斯分类的精确度。
- 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。
- Uses the modified self-training Bayes algorithm to recognize spam. 使用已修改的自学习模式算法识别垃圾邮件。
- 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。
- 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。
- 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。
- 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。
- For more information about how to use the Microsoft Naive Bayes Viewer, see Viewing a Mining Model with the Microsoft Naive Bayes Viewer. 有关如何使用Microsoft Naive Bayes查看器的详细信息,请参阅使用Microsoft Naive Bayes查看器查看挖掘模型。
- 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。
- Classification algorithms include the Microsoft Decision Trees, Microsoft Naive Bayes, and Microsoft Neural Network algorithms. 分类算法包括Microsoft决策树、Microsoft Naive Bayes和Microsoft神经网络算法。
