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naive bayesian classification
相关语句
  朴素贝叶斯分类
     Compared with Naive Bayesian Classification Model, experimental results show EANBC has higher accuracy.
     实验结果表明,与朴素贝叶斯分类模型相比,EANBC分类模型具有较高的分类正确率。
短句来源
     Research and Application of Naive Bayesian Classification Model
     朴素贝叶斯分类模型的研究与应用
短句来源
     It is mainly of two kinds, Naive Bayesian Classification and Bayesian Belief Network Classification.
     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。
短句来源
     2. 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.
     (2) 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。
短句来源
     2. A Naive Bayesian classification based on clustering principle(CNBC) by introducing clustering algorithm into Naive Bayesian classification.
     2.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)。
短句来源
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  “naive bayesian classification”译为未确定词的双语例句
     Research on Suspicious Financial Transaction Recognition Based on Naive Bayesian Classification
     基于朴素贝叶斯分类的可疑金融交易识别研究
短句来源
     After introducing the theory framework of Bayesian network, this paper carried out the following research: researching Bayesian network based on information geometry theory, improving the performance of naive Bayesian classification(NBC), retrieving information from semi-structured text using rules embedded dynamic Bayesian network, and hierarchical text classification model based on Bayesian network.
     本文在贝叶斯网络基础理论框架的基础上,主要研究了以下几个方面的内容:基于信息几何理论的贝叶斯网络研究、朴素贝叶斯分类器的提升、规则方法与贝叶斯网络结合文本信息抽取研究、层次贝叶斯网络文本分类器。
短句来源
     In this paper,a Boosting Classifier based on Naive Bayesian Classification was built and applied to classify the trajectories of MCS,using a dataset of environmental physical field values around MCS,based on the automated tracking of MCS over the Tibetan Plateau in summer from 1997 to 2000.Furthermore,results comparing several classification methods found the Boosting Bayesian Classifier to be comparable in performance with decision tree and neural network classifiers in the application of prediction of the trajectories of MCS.
     在朴素贝叶斯分类的基础上建立了一种增强型分类器系统,并在对1997~2002年夏季青藏高原上MCS(Mesoscale Convective System)进行自动追踪的基础上,对MCS的移动方向与其周边环境物理量场的分布特征进行了分类研究. 进而,将分类结果与决策树、人工神经网络分类方法进行了比较.
短句来源
  相似匹配句对
     Boosting Naive Bayesian Learning
     增强型朴素贝叶斯学习
短句来源
     Naive Bayesian Classiflers Using Feature Weighting
     基于特征加权的朴素贝叶斯分类器
短句来源
     Research and Application of Naive Bayesian Classification Model
     朴素贝叶斯分类模型的研究与应用
短句来源
     Research on the Approach of Classification in Data Mining Based on Naive Bayesian
     基于朴素贝叶斯的分类方法研究
短句来源
     On the naive art
     论素朴的艺术
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  naive bayesian classification
In this paper we present 1BC and 1BC2, two systems that perform naive Bayesian classification of structured individuals.
      


In database information classification, Naive Bayesian Classification Model is a simple but efficient solution. However, the hypothesis that its attributes should be independent prevents it from expressing the dependences among the attribute variables, which affects the efficiency of classification greatly. So common Bayesian network Model, which can express the dependencies among attribute variables, is more and more important. And yet, the learning algorithm of the structure of common...

In database information classification, Naive Bayesian Classification Model is a simple but efficient solution. However, the hypothesis that its attributes should be independent prevents it from expressing the dependences among the attribute variables, which affects the efficiency of classification greatly. So common Bayesian network Model, which can express the dependencies among attribute variables, is more and more important. And yet, the learning algorithm of the structure of common Bayesian classification model is NP-hard. In this paper, based on a simplified Bayesian network classification model, we apply its structure learning algorithm, with polynomial time complexity, to the classification of database information, and get a compromise between the learning efficiency and classification precision. The experimental result shows that this classification method has better performance in text retrieval of database information.

数据库信息分类中 ,朴素贝叶斯分类模型是一种简单而有效的分类方法 ,但它的属性独立性假设使其无法表达属性变量间存在的依赖关系 ,影响了它的分类性能 .而一般贝叶斯网络模型则由于能表达属性变量之间的依赖关系而越来越受到人们的重视 ,但一般贝叶斯网络分类模型结构的学习算法是一个NP完全问题 .本研究在一种简化的贝叶斯网络分类模型的基础上 ,利用其多项式时间复杂度的结构学习算法 ,将其应用于数据库信息分类 ,实现了学习效率和分类精度的一种折衷 .实验结果表明 ,这种分类方法有着比较高的数据库信息文本检索性能 .

The Naive Bayesian classification based on the Bayesian theorem is very popular. It approximates the Bayesian theorem by the assumption of class conditional independence. On this basis , Bayesian networks compensate for the assumption by adopting graphical models. On the other hand, it implies that the NP problem will occur in the process of classification. This paper adopts a middle way: It combines association rules with the ABN classification to construct a Bayesian...

The Naive Bayesian classification based on the Bayesian theorem is very popular. It approximates the Bayesian theorem by the assumption of class conditional independence. On this basis , Bayesian networks compensate for the assumption by adopting graphical models. On the other hand, it implies that the NP problem will occur in the process of classification. This paper adopts a middle way: It combines association rules with the ABN classification to construct a Bayesian classifier. It improves the independence assumption. Meanwhile, it avoids the NP problem. Finally we use our experimental results to show that it is better than the Naive Bayesian classifier in most fields.

NaiveBayes分类建立在贝叶斯理论基础上,应用极为广泛,它采用类条件独立假设对贝叶斯理论进行了近似。BayesianNetwork则在这一基础上采用图形模型弥补了独立假设的不足,同时揭示出分类过程中会导致NP问题的出现。本文采用一种折衷的方法--联合关联规则与ABN分类技术构造贝叶斯分类器。它弥补了独立假设的不足,同时也避免了解决NP问题。最后,本文用实验结果展示它在多个领域远远优于NaiveBayes分类器。

In this paper,a Boosting Classifier based on Naive Bayesian Classification was built and applied to classify the trajectories of MCS,using a dataset of environmental physical field values around MCS,based on the automated tracking of MCS over the Tibetan Plateau in summer from 1997 to 2000.Furthermore,results comparing several classification methods found the Boosting Bayesian Classifier to be comparable in performance with decision tree and neural network classifiers in the application of...

In this paper,a Boosting Classifier based on Naive Bayesian Classification was built and applied to classify the trajectories of MCS,using a dataset of environmental physical field values around MCS,based on the automated tracking of MCS over the Tibetan Plateau in summer from 1997 to 2000.Furthermore,results comparing several classification methods found the Boosting Bayesian Classifier to be comparable in performance with decision tree and neural network classifiers in the application of prediction of the trajectories of MCS.So it is proven to be an effective method to reveal the trajectories of MCS over the Tibetan Plateau and improve the accuracy of forecasting the disaster weather in Yangtze River Basin.

在朴素贝叶斯分类的基础上建立了一种增强型分类器系统,并在对1997~2002年夏季青藏高原上MCS(Mesoscale Convective System)进行自动追踪的基础上,对MCS的移动方向与其周边环境物理量场的分布特征进行了分类研究.进而,将分类结果与决策树、人工神经网络分类方法进行了比较.研究表明,与其他分类方法相比,使用增强型的贝叶斯分类器预测MCS的移动路径具有较好的效果,这为揭示高原上MCS的移动规律、提高长江中下游地区灾害天气预报的准确率提供了一种有效的方法.

 
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