The first approach is a simple Map-Reduce-enabled Naive Bayes classifier.
第一种方法是使用简单的支持Map - Reduce的Naive Bayes分类器。
Absrtact: An augmented naive Bayes classifier of Bayes classifier family is studied in this paper.
摘要：文中研究贝叶斯分类器家族中的一种扩展朴素贝叶斯分类器。
In this paper, we investigate enhancement of naive Bayes classifier using feature weighting technique.
该文利用特征加权技术来增强朴素贝叶斯分类器。
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）。
This paper takes Naive Bayes Classifier as an illustration to describe how to construct a prediction module in detail.
文章以朴素贝叶斯算法为例，详细描述了性能预测模块的构建过程。
In this paper, we investigate enhancement to naive Bayes classifier using feature weighting technique based on rough set theory.
本文基于粗糙集理论探索特征加权技术对朴素贝叶斯分类器的改进。
TAN classifier extends the structure of Naive Bayes classifier by adding augmenting arcs that obey certain structural restrictions.
TAN分类器按照一定的结构限制，通过添加扩展弧的方式扩展朴素贝叶斯分类器的结构。
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。
Naive Bayes classifier is a simple and effective classification method. Classifying based on Bayes Technology has got more and more attentions in the field of data mining.
朴素贝叶斯分类器是一种简单而高效的分类器，基于朴素贝叶斯技术的分类是当前数据挖掘领域的一个研究热点。
Naive Bayes classifier is a simple and effective classification method based on probability theory, but its attribute independence assumption is often violated in the real world.
朴素贝叶斯分类器是一种简单而有效的概率分类方法，然而其属性独立性假设在现实世界中多数不能成立。
It was the highlights of the paper that the method combined the explicit features and naive bayes classifier together to identify both of the encrypted and not encrypted P2P traffic.
着重介绍了采用明文特征和朴素贝叶斯分类相结合的方法，对加密的以及未加密的P 2 P流量进行识别。
Naive Bayes classifier is a simple and effective classification method, but its attribute independence assumption makes it unable to express the dependence among attributes in the real world.
朴素贝叶斯分类器是一种简单而高效的分类器，但是其属性独立性假设限制了对实际数据的应用。
If the NB conditional independence assumption actually holds, a Naive Bayes classifier will converge quicker than discriminative models like logistic regression, so you need less training data.
倘若条件独立性假设确实满足，朴素贝叶斯分类器将会比判别模型，譬如逻辑回归收敛得更快，因此你只需要更少的训练数据。
Multi-layer classifier Topic search engine Computer education resources Naive Bayes;
多层分类器； 垂直搜索引擎；计算机教育资源；朴素贝叶斯；
Multi-layer classifier Topic search engine Computer education resources Naive Bayes;
多层分类器； 垂直搜索引擎；计算机教育资源；朴素贝叶斯；
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