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hierarchical bayesian network
相关语句
  层次bayes网络
     Firstly, a new hierarchical Bayesian Network model is defined based on class hierarchical structure, which is used to represent large scale Bayesian network.
     首先使用类层次结构定义一种新的层次Bayes网络模型,用于表示大规模Bayes网络.
短句来源
     Finally, experiments in automatic detection and location of texts in images show the feasibility of this hierarchical Bayesian network and learning approach.
     最后将本层次Bayes网络及计算公式用于解决图像中文本的自动检测与定位问题,实验结果表明了它们的有效性.
短句来源
     Experiments in automatic detection and location of texts in images show the feasibility of this hierarchical Bayesian network and learning approach.
     将层次Bayes网络及计算公式用于解决图像中文本的自动检测与定位问题,实验结果表明了它们的有效性.
短句来源
  层次的bayes网络
     Firstly, a new hierarchical Bayesian Network model is defined based on class hierarchical structure,which is used to represent large scale Bayesian networks.
     首先使用类层次结构定义一种新的基于层次的Bayes网络模型,用于表示大规模Bayes网络.
短句来源
  相似匹配句对
     Hierarchical Bayesian Method for Forecast of Instrument Investment
     证券投资预测中的多层Bayes方法
短句来源
     Hierarchical Bayesian Analysis of Zero- failure Data
     无失效数据的多层 Bayes分析(英文)
短句来源
     ve Bayesian.
     ve Bayesian的贝叶斯网络的模型。
短句来源
     Bayesian Sequential Adjustment
     贝叶斯序贯平差
短句来源
     The Hierarchical Organization of the Networking
     网络化的层级组织
短句来源
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  hierarchical bayesian network
The resulting model is a hierarchical Bayesian network factored into modular component networks embedding variable-order Markov models.
      
This paper presents a method for the recognition of two-person interactions using a hierarchical Bayesian network (BN).
      
A hierarchical Bayesian network for event recognition of human actions and interactions
      
Using Bayesian Networks to model objects A priori knowledge about objects is captured in a hierarchical Bayesian network structure.
      
The hierarchical Bayesian network could preserve more information in the case of modular networks than in the case of scale-free networks.
      
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A learning approach is proposed to solve the problems of conditional probability assignation in large scale Bayesian network. Firstly, a new hierarchical Bayesian Network model is defined based on class hierarchical structure, which is used to represent large scale Bayesian network.Then, the train data set is changed from a single table to a database composed of some database tables. And each database table corresponds to a Bayesian network block. Based on that,...

A learning approach is proposed to solve the problems of conditional probability assignation in large scale Bayesian network. Firstly, a new hierarchical Bayesian Network model is defined based on class hierarchical structure, which is used to represent large scale Bayesian network.Then, the train data set is changed from a single table to a database composed of some database tables. And each database table corresponds to a Bayesian network block. Based on that, a formula of conditional probability is developed. And each conditional probabilistic table of Bayesian network block can be calculated from the database tables respectively. Properly adjust the attribute number in each database table can assure the validity of this learning approach. Finally, experiments in automatic detection and location of texts in images show the feasibility of this hierarchical Bayesian network and learning approach.

针对大规模Bayes网络的条件概率赋值问题,提出一种学习方法.首先使用类层次结构定义一种新的层次Bayes网络模型,用于表示大规模Bayes网络.然后将训练数据集由单个数据表的形式转化成多表数据库,其中每个数据库表对应1个Bayes网络模块.在此基础上导出条件概率计算公式,从每个数据库表中算出相应的Bayes网络模块的条件概率表,由此实现对整个层次Bayes网络的概率赋值.可通过适当增加数据库表的数目来控制每个表中属性的个数,保证计算的可行性.最后将本层次Bayes网络及计算公式用于解决图像中文本的自动检测与定位问题,实验结果表明了它们的有效性.

A learning approach is proposed to solve the problems of conditional probability assignation in large scale Bayesian networks.Firstly, a new hierarchical Bayesian Network model is defined based on class hierarchical structure,which is used to represent large scale Bayesian networks.Then,the train data set is changed from a single table to a database composed of some database tables.And each database table corresponds to a Bayesian network block.Based on that,a...

A learning approach is proposed to solve the problems of conditional probability assignation in large scale Bayesian networks.Firstly, a new hierarchical Bayesian Network model is defined based on class hierarchical structure,which is used to represent large scale Bayesian networks.Then,the train data set is changed from a single table to a database composed of some database tables.And each database table corresponds to a Bayesian network block.Based on that,a formula of conditional probability is developed.And each conditional probabilistic table of Bayesian network block can be calculated from the database tables respectively.Proper adjustment of the attribute number in each database table can assure the validity of this learning approach.Experiments in automatic detection and location of texts in images show the feasibility of this hierarchical Bayesian network and learning approach.

针对大规模Bayes网络的条件概率赋值问题,提出一种学习方法.首先使用类层次结构定义一种新的基于层次的Bayes网络模型,用于表示大规模Bayes网络.然后将训练数据集由单个数据表的形式转化成多表数据库,其中每个数据库表对应一个Bayes网络模块.在此基础上导出条件概率计算公式,从每个数据库表中算出相应的Bayes网络模块的条件概率表,由此实现对整个层次Bayes网络的概率赋值.通过适当增加数据库表的数目来控制每个表中属性的个数,保证计算的可行性.将层次Bayes网络及计算公式用于解决图像中文本的自动检测与定位问题,实验结果表明了它们的有效性.

A semantic modeling approach for medical image semantic retrieval based on hierarchical Bayesian networks was proposed,in a small set of samples. SVMs (support vector machines) was used to map low-level image features into object semantics,then high-level semantics was captured through fusing these object semantics using a Bayesian network.A multi-layer medical image semantic model was built to aim to enable automatic image annotation and semantic retrieval by using various Keywords at different...

A semantic modeling approach for medical image semantic retrieval based on hierarchical Bayesian networks was proposed,in a small set of samples. SVMs (support vector machines) was used to map low-level image features into object semantics,then high-level semantics was captured through fusing these object semantics using a Bayesian network.A multi-layer medical image semantic model was built to aim to enable automatic image annotation and semantic retrieval by using various Keywords at different semantic levels.To validate the method,a multi-level semantic model was built from a small set of astrocytona MRI (magnetic resonance imaging) samples,in order to extract semantics of astrocytona in malignant degree. Experiment results show that this approach is effective to enable multi-level interpretation of astrocytona MRI.It out performs the Bayesian network-based models using k-nearest neighbor classifiers (K-NN) or Gaussion mixture models (GMM).

提出一种在小样本的情况下,基于多层贝叶斯网络的医学图像语义建模方法.该方法采用支持向量机实现从低层视觉特征到对象语义的映射,使用贝叶斯网络融合对象语义,提取高级语义,从而建立一个多层医学图像语义模型,可支持多层次的医学图像语义自动标注及其检索.将该方法用于星形细胞瘤恶性程度的语义提取,并建立一个多层语义模型.实验表明,该模型与使用K近邻分类器或高斯混合模型取代SVM的语义模型相比,查全率有明显的提高.

 
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