Weighted naive Bayes is it extension.
加权朴素贝叶斯是对它的一种扩展。
Naive Bayes is easy to implement and fast, so it is widely used.
其中朴素贝叶斯具有容易实现，运行速度快的特点，被广泛使用。
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。
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；
On the other hand, Naive Bayes is weighted by computing the confidence of association rules.
另一方面，通过关联规则的置信度，给朴素贝叶斯加权。
Naive Bayes is an algorithm that can be used to classify objects into usually binary categories.
朴素贝叶斯算法，可使用对象进行分类，通常是二进制类。
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。
A simple machine learning algorithm called naive Bayes can separate legitimate email from spam email.
一个简单的机器学习算法，朴素贝叶斯算法可以把正规邮件从垃圾邮件里面分离出来。
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。
Although the Naive Bayes spam filter is simple and convenient, the recall and precision are hard to be improved.
虽然朴素贝叶斯邮件过滤器计算简便，但召回率和正确率都难以进一步提高。
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。
However, for this article, I'll show only the Naive Bayes approach, because it demonstrates the overall problem and inputs in Mahout.
但在本文中，我只会演示Naive Bayes方法，因为这能让您看到总体问题和Mahout中的输入。
The experiment of Naive Bayes classification indicates that this method can effectively improve classification precision of Chinese texts.
基于朴素贝叶斯分类方法的实验表明，提出的方法能够有效提高中文文本的分类准确率。
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 classifiers often break down when the size of the training examples per class are not balanced or when the data is not independent enough.
当各类的训练示例的大小不平衡，或者数据的独立性不符合要求时，Naive Bayes分类器会出现故障。
Many algorithms are used to create supervised learners, the most common being neural networks, Support Vector Machines (SVMs), and Naive Bayes classifiers.
创建监管学习程序需要使用许多算法，最常见的包括神经网络、SupportVectorMachines (SVMs)和Naive Bayes分类程序。
Otherwise, information extracting, information preprocessing technique, inquiry interface, and information filter technique based on naive bayes is put forward.
本文还讲述了信息提取技术、信息预处理技术、查询接口实现技术、基于朴素贝叶斯的信息过滤技术等关键技术。
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 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。
Naive Bayes algorithm is a simple and effective classification algorithm. However, its classification performance is affected by its conditional attribute independence assumption.
朴素贝叶斯算法是一种简单而高效的分类算法，但是它的条件独立性假设影响了其分类性能。
This paper focuses on privacy preserving classification, and presents a privacy preserving Naive Bayes 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分类中的隐私保护方法。
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 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。
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。
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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