加权朴素贝叶斯是对它的一种扩展。
另一方面，通过关联规则的置信度，给朴素贝叶斯加权。
On the other hand, Naive Bayes is weighted by computing the confidence of association rules.
朴素贝叶斯算法，可使用对象进行分类，通常是二进制类。
Naive Bayes is an algorithm that can be used to classify objects into usually binary categories.
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。
Naive Bayes is easy to implement and fast, so it is widely used.
然后本文在朴素贝叶斯预测模块的基础上，对预测方法进行拓展。
Furthermore, this paper extends the prediction method on the basis of Bayes module.
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。
This paper takes Naive Bayes Classifier as an illustration to describe how to construct a prediction module in detail.
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;
实验结果表明，与传统的朴素贝叶斯算法相比，该方法具有更好的性能。
The experimental results show that this algorithm has better performance when compared with traditional naive Bayesian algorithm.
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。
Although the Naive Bayes spam filter is simple and convenient, the recall and precision are hard to be improved.
它主要有两种分类方法：一种为朴素贝叶斯分类，另一种为贝叶斯信念网络分类。
It is mainly of two kinds, Naive Bayesian Classification and Bayesian Belief Network Classification.
地物类别的稳定性是BP识别算法效率的瓶颈，导致其健壮性不如朴素贝叶斯网络。
The stability of object types is one bottleneck in BP, leading to its robustness less than naive Bayesian network.
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.
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。
A simple machine learning algorithm called naive Bayes can separate legitimate email from spam email.
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。
This paper USES the improved K-means (IKM) algorithm to process the missing data and thus improve the precision of the Naive Bayes classifier.
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。
The experiment of Naive Bayes classification indicates that this method can effectively improve classification precision of Chinese texts.
在垃圾邮件分类和朴素贝叶斯算法研究的基础上，提出了基于用户知识的贝叶斯分类算法。
An user knowledge based na? Ve bayes classifier was proposed in order to conquer the problem that most of the E-mail is unstructured and need users decoding.
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。
Naive Bayes algorithm is a simple and effective classification algorithm. However, its classification performance is affected by its conditional attribute independence assumption.
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。
We extended the Naive Bayesian Classifier, applied it in the relational classification filed, and introduced the concept of user's guidance.
将聚类算法引入到朴素贝叶斯分类研究中，提出一种基于聚类的朴素贝叶斯分类算法(CNBC)。
A Naive Bayesian classification based on clustering principle (CNBC) by introducing clustering algorithm into Naive Bayesian classification.
对数据挖掘中的贝叶斯分类技术进行了讨论，重点分析了朴素贝叶斯分类技术的基本原理和工作过程。
The Bayesian classification technology in Data mining was discussed, and the research was emphasized on the basic principle and the work procedure of the Naive Bayesian classification technology.
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。
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.
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.
文中针对该算法这两个最主要的缺陷，提出增量学习概念，引入损失幅度参数，改进和完善朴素贝叶斯分类算法。
Then in allusion to these two important factors, a concept of incremental learning and a loss extent parameter are put forward in this paper, and Native Bayesian Classification.
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. Classifying based on Bayes Technology has got more and more attentions in the field of data mining.
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。
Otherwise, information extracting, information preprocessing technique, inquiry interface, and information filter technique based on naive bayes is put forward.
树扩展型朴素贝叶斯（TAN）分类器放松了朴素贝叶斯的属性独立性假设，是对朴素贝叶斯分类器的有效改进。
TAN(tree augmented Nave Bayes) takes the Nave Bayes classifier and adds edges to it, it is efficient extend of Nave Bayes.
该技术通过引入用户反馈机制，使用改进的朴素贝叶斯方法，构建面向特定用户的过滤器，从而进行垃圾邮件过滤。
By introducing the novel users' feedback mechanism, the technique adopts an improved Na? Ve Bayesian approach to construct classifiers for specific users to fulfill spam filtering.
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.
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。
This paper focuses on privacy preserving classification, and presents a privacy preserving Naive Bayes classification approach based on data randomization and feature reconstruction.
一方面，通过对关联规则的挖掘，发现条件属性之间的关联关系，并且利用这种关联关系弱化朴素贝叶斯的独立性假设；
On the one hand, the associated relationship between condition attributes can be found out through association rules mining, in order to weaken the independent assumption.
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