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）。
Most of the content-based filtering algorithms are based on vector space model, of which Naive Bayes algorithm and K-Nearest Neighbor (KNN) algorithm are widely used.
基于内容的过滤算法大多数是基于向量空间模型的算法，其中广泛使用的是朴素贝叶斯算法和K最近邻（KNN）算法。
Naive Bayes classification is a kind of simple and effective classification model. However, the performance of this model may be poor due to the assumption on the condition independence.
朴素贝叶斯分类是一种简单而高效的分类模型，然而条件独立性假设在现实中很少出现，致使其性能有所下降。
This article introduced the theory of naive Bayes and discussed two popular models: multinomial model (MM) and Bernoulli model (BM) in details, implemented runnable code and performed some data tests.
本文详细介绍了朴素贝叶斯的基本原理，讨论了两种常见模型：多项式模型(MM)和伯努利模型(BM)，实现了可运行的代码，并进行了一些数据测试。
This article introduced the theory of naive Bayes and discussed two popular models: multinomial model (MM) and Bernoulli model (BM) in details, implemented runnable code and performed some data tests.
本文详细介绍了朴素贝叶斯的基本原理，讨论了两种常见模型：多项式模型(MM)和伯努利模型(BM)，实现了可运行的代码，并进行了一些数据测试。
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