We construct the privacy preserving Naive Bayesian Classifier.
构造了保持隐私的朴素贝叶斯分类器。
This paper investigates the credit scoring accuracy of three naive Bayesian classifier models.
论文研究了三种朴素贝叶斯分类器信用评估模型的精度。
We extended the Naive Bayesian Classifier, applied it in the relational classification filed, and introduced the concept of user's guidance.
对朴素贝叶斯分类算法进行拓展，使其应用到多关系数据分类领域，并引入了用户指导的概念。
The oblivious polynomial evaluation protocol will be used many times in our privacy preserving naive bayesian classifier, so its efficiency is important to the solution.
构造的思路是先将其转化为相应的泰勒展开式，然后使用健忘多项式计算协议获得结果。
Absrtact: Naive Bayesian classifier is a simple and effective classifier, but its conditional independence assumption makes it unable to express the dependence among features.
摘要：朴素贝叶斯分类器是一种简单而高效的分类器，但它的条件独立性假设使其无法表示属性问的依赖关系。
So a new Bayesian model mixed tree augmented Naive Bayes classifier(MTANC) based on the rough set theory is presented.
因此，提出了一种基于粗糙集理论的混合树增广朴素贝叶斯分类模型（MTANC）。
So a new Bayesian model mixed tree augmented Naive Bayes classifier(MTANC) based on the rough set theory is presented.
因此，提出了一种基于粗糙集理论的混合树增广朴素贝叶斯分类模型（MTANC）。
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