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   bayesian belief network 的翻译结果: 查询用时:0.181秒
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bayesian belief network     
相关语句
  贝叶斯网络
     Knowledge discovery in databases (KDD): A study of the association between the effect of“Blood-activating and Stasis-eliminating”in 53 kinds of TCM and 17 kinds of activities was carried out by Bayesian belief network analysis technique after the terms of effects and activities were classified and standarded, and the results were showed by directed acycline graph and conditional probability tables. The correct classification rate of the method is 89.4737%.
     数据库知识发现研究:本文在对中药功效和药理词汇分类整理的基础上,利用贝叶斯网络数据挖掘技术,研究了53味常用中药的“活血化瘀”功效与17个药理指标之间的关联关系,并给出相应的拓扑结构图和条件概率表,方法的正确率是89.4737%。
短句来源
     Bayesian Network(BN),also called Bayesian belief network,are widely-used approaches in probability theory at all times. With the development of the semantic web,ontology has become widely used to represent the conceptualization of a domain.
     贝叶斯网络(Bayesian Network),也称贝叶斯置信网络,一直以来都是概率理论中的普遍方法。
短句来源
  贝叶斯信念网络
     Algorithm of Bayesian Belief Network Structure of Compressed Candidature
     压缩候选的贝叶斯信念网络构造算法
短句来源
     The Algolrithm of Data Classification Unearth Based on Bayesian Belief Network
     基于贝叶斯信念网络的数据分类挖掘算法
短句来源
     There are many techniques for data classification such as the decision tree, Bayesian belief network, association-based classification, genetic algorithms, K-nearest neighbor classifiers, neural network, etc.
     挖掘分类模式的方法有多种,如决策树方法、贝叶斯信念网络、基于关联的分类方法、遗传算法、K-最临近方法、神经网络等等。
短句来源
  贝叶斯网
     Bayesian Belief Network Model Learning,Inference and Applications
     贝叶斯网模型的学习、推理和应用
短句来源
     A Bayesian Belief Network(BBN)is a graphic model that encodes joint probability distribution among uncertain variables,it express a potential dependent relationship between variables.
     贝叶斯网是用来表示不确定变量集合联合概率分布的图形模式,它反映了变量间潜在的依赖关系。
短句来源
     Modeling with Bayesian belief network has been a powerful tool to solve many uncertainty problems.
     使用贝叶斯网建模已成为解决许多不确定性问题的强有力工具。
短句来源
     Based on the latest researched results at home and abroad,this paper reviews the learning,inference and applications of the Bayesian Belief Network,and presents possible future research orientations.
     基于国内外最新的研究成果对贝叶斯网模型的学习、推理和应用情况进行了综述,并对未来的发展方向进行了展望。
短句来源
     For some special Bayesian Belief Network such as polytree network has had some feasible arithmetic of probability inference,but up to now,there are not feasible arithmetic of logic inference.
     对于一些特殊的贝叶斯网(如多树型网络)已经有了一些可行的概率推理的算法,但到目前为止,还没有可行的逻辑推理的算法。
短句来源
  斯信念网络
     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
     Algorithm of Bayesian Belief Network Structure of Compressed Candidature
     压缩候选的贝叶斯信念网络构造算法
短句来源
     The Algolrithm of Data Classification Unearth Based on Bayesian Belief Network
     基于贝叶斯信念网络的数据分类挖掘算法
短句来源
     There are many techniques for data classification such as the decision tree, Bayesian belief network, association-based classification, genetic algorithms, K-nearest neighbor classifiers, neural network, etc.
     挖掘分类模式的方法有多种,如决策树方法、贝叶斯信念网络、基于关联的分类方法、遗传算法、K-最临近方法、神经网络等等。
短句来源

 

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      bayesian 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 used a Bayesian belief network (BBN) to model a clinical pathway for radical prostatectomy and to categorize patient's length of stay (LOS) as being met or delayed given the patient's outcomes and activities.
          
    Using a Bayesian belief network model to categorize length of stay for radical prostatectomy patients
          
    The approach uses phase-type distributions conditioned on a Bayesian belief network.
          
    We describe how a Bayesian belief network models the behaviour of geriatric patients using predictive variables: personal details, admission reasons and dependency levels.
          
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    Controlling risk is a very important part in software-intensive project management.Based on studying on the relationships among resources,user requirement and the process product,a software project risks management model built on user requirements is put forward in the paper.And by analysis correlative software engineering technologies,the meth-ods that software engineering technologies can be applied to support model are illuminated.In the last part of the pa-per,a techneque utilizing Bayesian Belief...

    Controlling risk is a very important part in software-intensive project management.Based on studying on the relationships among resources,user requirement and the process product,a software project risks management model built on user requirements is put forward in the paper.And by analysis correlative software engineering technologies,the meth-ods that software engineering technologies can be applied to support model are illuminated.In the last part of the pa-per,a techneque utilizing Bayesian Belief Networks(BBN)that can realize risk forecasting and management by reasoning is discussed.

    控制软件项目的风险是软件项目管理的重要组成部分。目前的软件风险管理方法存在着一些不足,在软件项目管理实践中不能取得最佳效果。文章通过对软件产品开发中资源、用户需求和产品之间的内在关系的分析,提出了基于用户需求的软件项目风险管理模型,该模型从用户需求角度出发,通过软件过程技术、产品工程技术和度量技术的支持可以有效地控制软件项目风险,保证了软件产品满足用户需求的能力,从而使软件项目达到成功。在模型的基础上,文章对实现模型的技术进行了研究,给出了模型的BayesianBeliefNetworks实现方法。

    In recent years,uncertainty reasoning in Artificial Intelligence has been a focus of research.A Bayesian Belief Network(BBN)is a graphic model that encodes joint probability distribution among uncertain variables,it express a potential dependent relationship between variables.Modeling with Bayesian belief network has been a powerful tool to solve many uncertainty problems.Based on the latest researched results at home and abroad,this paper reviews the learning,inference and applications of...

    In recent years,uncertainty reasoning in Artificial Intelligence has been a focus of research.A Bayesian Belief Network(BBN)is a graphic model that encodes joint probability distribution among uncertain variables,it express a potential dependent relationship between variables.Modeling with Bayesian belief network has been a powerful tool to solve many uncertainty problems.Based on the latest researched results at home and abroad,this paper reviews the learning,inference and applications of the Bayesian Belief Network,and presents possible future research orientations.

    近年来在人工智能领域,不确定性问题一直成为人们关注和研究的焦点。贝叶斯网是用来表示不确定变量集合联合概率分布的图形模式,它反映了变量间潜在的依赖关系。使用贝叶斯网建模已成为解决许多不确定性问题的强有力工具。基于国内外最新的研究成果对贝叶斯网模型的学习、推理和应用情况进行了综述,并对未来的发展方向进行了展望。

    The simulated RoboCup is a complex, dynamic multi-agent system full of uncertainty. In such an environment that the state-space complexity restricts the designer's ability, the designer can not supply agents with complete and exact information about the correct response at any state. So learning, teamwork and opponent modeling become the three challenging problems in RoboCup, and planning is playing an important role in these challenges. How to plan activities to influence adversary's actions based on situation...

    The simulated RoboCup is a complex, dynamic multi-agent system full of uncertainty. In such an environment that the state-space complexity restricts the designer's ability, the designer can not supply agents with complete and exact information about the correct response at any state. So learning, teamwork and opponent modeling become the three challenging problems in RoboCup, and planning is playing an important role in these challenges. How to plan activities to influence adversary's actions based on situation assessment in the dynamic, real-time environment must be solved now. In this paper, we propose a dynamic planning model on the basis of effect-based operations. Agents can select and execute actions from their own experiences based on the analysis of the situation. Furthermore, the system has life-long learning ability. We integrate bayesian belief network and case-based reasoning to implement it. Experimental results show that the agents' adaptation has been improved.

    如何提高agent的学习能力、对手建模能力以及多agent团队运作能力是目前RoboCup研究所面临的3项挑战,在上述的挑战中,行为规划起了非常重要的作用.agent如何能够在动态实时的复杂环境中根据场景变化来动态规划自己的行为是RoboCup目前急需解决的问题.提出一种面向效果操作方法的动态行为规划模型,使队员能够在场景分析的基础上,根据经验动态选择和执行行为策略,且具有持续学习的能力.采用贝叶斯信念网络和基于示例推理相结合的方法来实现.实验结果表明,该方法有效提高了队员适应环境的能力.

     
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