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bayesian network classification
相关语句
  贝叶斯网络分类
     APPLICATION OF BAYESIAN NETWORK CLASSIFICATION TO REMOTE SENSING CHANGE DETECTION
     贝叶斯网络分类算法在遥感数据变化检测上的应用
短句来源
     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.
     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 .
短句来源
     The experimental result indicates that the post classification comparison based on Bayesian Network classification algorithm is a newly effective approach for remote sensing imageries change detection.
     实验结果表明:基于贝叶斯网络分类算法的后分类比较变化检测方法是遥感影像变化检测的一种新的有效方法。
短句来源
     Based on above all, it focuses on several key points in Bayesian learning: Bayesian network classification model, active Bayesian classifier, text mining based on Bayesian latent semantic analysis and clustering analysis based on Bayesian model selection.
     在此基础上,主要研究了贝叶斯学习理论中的几个关键问题:贝叶斯网络分类模型、主动贝叶斯分类器、基于贝叶斯潜在语义索引的文本挖掘和基于贝叶斯模型选择的聚类分析。
短句来源
  “bayesian network classification”译为未确定词的双语例句
     Remote Sensing Monitoring of Soil Salinization Based on Bayesian Network Classification
     基于贝叶斯网络分类的土壤盐渍化遥感监测
短句来源
     Remote Sensing Monitoring of Soil Salinization Based on Bayesian Network Classification.
     基于贝叶斯网络分类的土壤盐渍化遥感监测
短句来源
     Taking the Landsat TM data acquired on 1996-05-29 and 2001-05-19 in Beijing as an example, the Bayesian Network classification algorithm is introduced in detail and then the change detection using the temporal remote sensing data is realized. The result indicates that the post classification comparison based on Bayesian Network classification algorithm is a newly effective approach for remote sensing change detection.
     以北京通州地区1996年5月29日和2001年5月19日2个时期的遥感影像为例介绍了基于贝叶斯网络 (BN)的分类算法,在此基础上实现了2个不同实现遥感影像的变化检测,实验结果表明:基于BN分类的后分类比较变 化检测方法是遥感数据变化检测的一种新的有效方法.
短句来源
     The remote sensing data Bayesian networks structure training involves the prior knowledge and amount of samples,which is important tache of Bayesian network classification.
     遥感数据的贝叶斯网络结构训练涉及先验知识和样本数量两个方面,是贝叶斯网络结构分类的重要环节。
短句来源
     This paper depends on the application target and remote sensing bands physical meaning,does the experiment of different combinations of remote sensing bands and amount of samples,the experiment results can provide basic guidelines to the Bayesian network classification.
     该文以应用目标和遥感数据波段的物理意义为先验知识指导,进行了贝叶斯网络结构建立中的遥感数据波段数和样本数的优化组合实验,为贝叶斯网络在遥感数据分类方面提供了基础性实验结果。
短句来源
  相似匹配句对
     Research on Bayesian Network
     贝叶斯网络研究
短句来源
     THE APPLICATION OF THE BAYESIAN NETWORK METHOD TO AIRBORNE DATA CLASSIFICATION
     航空遥感数据的贝叶斯网络分类
短句来源
     The application of Bayesian network model in database information classification
     数据库信息分类中贝叶斯网络模型的应用
短句来源
     APPLICATION OF BAYESIAN NETWORK CLASSIFICATION TO REMOTE SENSING CHANGE DETECTION
     贝叶斯网络分类算法在遥感数据变化检测上的应用
短句来源
     The Study on Remote Sensing Data Classification Using Bayesian Network
     遥感数据的贝叶斯网络分类研究
短句来源
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  bayesian network classification
As was reviewed earlier in this section, several Bayesian network classification models have been proposed.
      
A combination is also in study, between the iterative process of the relaxation and the Bayesian Network classification.
      


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...

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 .

Taking the Landsat TM data acquired on 1996-05-29 and 2001-05-19 in Beijing as an example, the Bayesian Network classification algorithm is introduced in detail and then the change detection using the temporal remote sensing data is realized. The result indicates that the post classification comparison based on Bayesian Network classification algorithm is a newly effective approach for remote sensing change detection.

以北京通州地区1996年5月29日和2001年5月19日2个时期的遥感影像为例介绍了基于贝叶斯网络 (BN)的分类算法,在此基础上实现了2个不同实现遥感影像的变化检测,实验结果表明:基于BN分类的后分类比较变 化检测方法是遥感数据变化检测的一种新的有效方法.

In recent years,the Bayesian network has been used in many study fields as a data-mining tool,but the study using it to process remote sensing data is very seldom seen.Bayesian network classification expresses the dependence of input data and classification result,the network structure can well express the state of conditional probability between different nodes.The remote sensing data Bayesian networks structure training involves the prior knowledge and amount of samples,which...

In recent years,the Bayesian network has been used in many study fields as a data-mining tool,but the study using it to process remote sensing data is very seldom seen.Bayesian network classification expresses the dependence of input data and classification result,the network structure can well express the state of conditional probability between different nodes.The remote sensing data Bayesian networks structure training involves the prior knowledge and amount of samples,which is important tache of Bayesian network classification.This paper depends on the application target and remote sensing bands physical meaning,does the experiment of different combinations of remote sensing bands and amount of samples,the experiment results can provide basic guidelines to the Bayesian network classification.

贝叶斯网络表达了输入数据与分类结果之间的依赖关系,网络结构则表达了节点之间的条件概率状态。遥感数据的贝叶斯网络结构训练涉及先验知识和样本数量两个方面,是贝叶斯网络结构分类的重要环节。该文以应用目标和遥感数据波段的物理意义为先验知识指导,进行了贝叶斯网络结构建立中的遥感数据波段数和样本数的优化组合实验,为贝叶斯网络在遥感数据分类方面提供了基础性实验结果。

 
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