The first approach is a simple Map-Reduce-enabled Naive Bayes(NB) classifier. 第一种方法是使用简单的支持Map-Reduce的NaiveBayes分类器。
This paper focuses on privacy preserving classification, and presents a privacy preserving Naive Bayes(NB) classification approach based on data randomization and feature reconstruction. 围绕着分类挖掘中的隐私保护问题展开研究,给出了一种基于数据处理和特征重构的朴素贝叶斯(NB)分类中的隐私保护方法。
Then by Naive Bayes(NB) text classification method, a document unknown class can be classified. 然后采用贝叶斯文本分类方法对未知类别文档进行分类。
However, for this article, I 'll show only the Naive Bayes(NB) approach, because it demonstrates the overall problem and inputs in Mahout. 但在本文中,我只会演示NaiveBayes方法,因为这能让您看到总体问题和Mahout中的输入。
On the other hand, Naive Bayes(NB) is weighted by computing the confidence of association rules. 另一方面,通过关联规则的置信度,给朴素贝叶斯(NB)加权。