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belief network     
相关语句
  信度网
     The inference algorithm is the basis of learning and application in belief network.
     推理算法是信度网学习和应用的基础。
短句来源
     The Research on Prediction and Control of Telecommunication Network Congestion Based on the Belief Network Model
     基于信度网模型的电信网阻塞预测及控制的研究
短句来源
     Study on Transition from Causality Diagram to Belief Network
     因果图向信度网转化的方法研究
短句来源
     Uncertainty knowledge representation can be classified into two categories: probabilistic and non-probabilistic. The probabilistic approaches include the Belief Network, the dynamic causality diagram, the Markov network, the approach used in PROSPECTOR, etc.
     不确定知识表达的方法可分为两大类:一类是基于概率的方法,包括信度网(Belief Network)、动态因果图(Dynamic Causality Diagrams)、马尔可夫网(Markov Network)以及在专家系统PROSPECTOR中使用的方法等。
短句来源
     Approximate Inference Algorithms of Belief Network(1)
     信度网近似推理算法(上)
短句来源
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  信念网络
     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
     A Belief Network Retrieval Model Expanded with Synonym-based Evidence
     一个基于同义词证据扩展的信念网络检索模型(英文)
短句来源
     This paper presents an IR model that extends the basic belief network model with synonym-based evidence.
     以同义词为证据扩展基本信念网络模型,得到一个扩展的信念网络检索模型。
短句来源
     Combining distinct sources of evidential knowledge to expand belief network for information retrieval (IR) is a current and important research direction.
     归并不同证据资源扩展用于信息检索的信念网络是当前一个重要的研究方向。
短句来源
     The nakve Bayesian classification model and the Bayesian belief networks model are introduced. The learning of a belief network is discussed.
     介绍了基本的贝叶斯分类模型和贝叶斯信念网络模型,对网络模型的学习进行了讨论。
短句来源
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  信度网络
     Belief Network Method in Uncertainty Management
     不精确推理中的信度网络方法
短句来源
  置信度网络
     APPLICATION OF BELIEF NETWORK IN PATTERN RECOGNITION CLASSIFIER
     置信度网络在判别分析中的应用
短句来源

 

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  belief network
The method uses a Bayesian Belief Network to model the software inspection process and calculates the inference on how effective a particular inspection was.
      
We present a number of examples of graphical models, including the QMR-DT database, the sigmoid belief network, the Boltzmann machine, and several variants of hidden Markov models, in which it is infeasible to run exact inference algorithms.
      
Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases.
      
Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases.
      
Belief network and object-oriented technology are employed to help the system reason and reconfigure itself.
      
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A Knowledge Acquisition Model for diagnostic problem solving, termed multilevel belief network (MBN), is presented. It classifies the diagnosis knowledge into difficult and easy ones described by causal belief network and diagnostic belief network respectively. In MBN, three "knowledge chunk" strategies are employed, that is, goal first strategy, evidence first strategy and case synthesis strategy. The goal first strategy links the belief network first from a goal of diagnostic problem...

A Knowledge Acquisition Model for diagnostic problem solving, termed multilevel belief network (MBN), is presented. It classifies the diagnosis knowledge into difficult and easy ones described by causal belief network and diagnostic belief network respectively. In MBN, three "knowledge chunk" strategies are employed, that is, goal first strategy, evidence first strategy and case synthesis strategy. The goal first strategy links the belief network first from a goal of diagnostic problem solving, the evidence first strategy links the belief network first,from facts about diagnostic problem solving and case synthesis strategy forms the belief network through an analysis of a series of diagnosis examples. The advantage of these strategies lies in keeping the coherence and would be easily accepted by domain experts during knowledge acquistion. In order to illustrate the application of MBN, an interactive knowledge acquisition system KAS-CEI based on MBN is given. KAS-CEI has several independent modules such as knowledge transferring module, concept analysis module, completeness checking module, knowledge base generation module and knowledge base testing module. It supports various inference mechanisms including deductive inference, abductive inference and mixture inference, and it is also capable of forming various knowledge bases with the rule, frame and semantic network representation.

本文从诊断问题求解知识的一种组织形式——证据网络出发,讨论了将领域知识转换成证据网络的各种策略,并由此提出了知识获取的多层证据网络模型。根据该模型我们开发了交互式知识获取系统KAS-CEI,并将该系统用于汽车发动机故障诊断专家系统(AEFDES)知识库的建造。

This paper considers the structuring causal tree models in Belief Networks with continuous variables. It is shown that if a collection of probabilisticly coupled variables are governed by a joint normal distribution and tree structured representation exists, then both the topology and all internal relationships of the tree can be structured by observing pairwise dependencies among the available variables (i.e, the leaves of the tree). Furthermore, the constraints for normal distributed variables are less...

This paper considers the structuring causal tree models in Belief Networks with continuous variables. It is shown that if a collection of probabilisticly coupled variables are governed by a joint normal distribution and tree structured representation exists, then both the topology and all internal relationships of the tree can be structured by observing pairwise dependencies among the available variables (i.e, the leaves of the tree). Furthermore, the constraints for normal distributed variables are less than that for bi-valued variables.

本文研究在命题变量连续取值的信度网络中建立因果树模型的问题.我们指出:如果信度网络中的所有命题变量服从于某个可树分解的联合正态分布,则由网络可建立因果树.该树的拓扑结构以及树中所有非叶节点之间的概率依赖关系皆可由叶节点之间的两两相关系数确定;并且,为了由网络建立因果树,对于正态分布变量的限制要比两值分布变量的限制松.文中还给出了建立因果树的方法和步骤.

The paper discusses the applications of influence diagrams to probabilistic inference, and information with the inference. The concept and method of quasi information filtering are proposed. The fusion of information in belief network is eramed and studied by using quasi-in-formation filtering to represent the flexible inference machanism in human's actions. A flexible inference rule in belief networks is designed and quasi-information filtering provides a new approach to studying uncertainty in...

The paper discusses the applications of influence diagrams to probabilistic inference, and information with the inference. The concept and method of quasi information filtering are proposed. The fusion of information in belief network is eramed and studied by using quasi-in-formation filtering to represent the flexible inference machanism in human's actions. A flexible inference rule in belief networks is designed and quasi-information filtering provides a new approach to studying uncertainty in decision analysis and A.I.

本文讨论了影响图理论在概率推理过程中的应用和要涉及的信息及信息量。提出了虚拟信息滤波过程的概念和方法;讨论了概念网络中的信息溶合和衍殖过程;研究了用虚拟滤波过程表征人们行为中的柔性推理机制;设计了概念网络上的一种柔性推理准则。为决策分析和人工智能领域中的不肯定性信息测度问题提供了一种新的方法。

 
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