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。
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.
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.
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。
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.
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。
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.
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