bayesian belief network中文是什么意思

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中文翻译
造句

[网络] 贝叶斯信念网络；贝叶斯信任度网络；贝叶斯置信网络

bayesian network 贝式网路; 贝式网络; 贝叶斯网络
bayesian inference network 贝斯推理网络; 贝叶斯推理网络
bayesian 贝叶斯的; 贝叶斯定理的
belief n. 1.信，信任；相信 (in); 信仰，信心。 2.信念；意见。 3.【基督新教】信条；教义；[the B-]使徒信条。 He has no great belief in religion 他不大相信宗教。 a man worthy of belief 可以信得过的人。 a person light of belief 轻信的人。 My belief is that …我相信，在我看来。 beyond belief 难以置信；非常，想像以外地。 in the belief that …相信…。 to the best of my belief 我相信，以我看来。
belief in 信任于
in the belief that 相信，认为，对
of belief 信仰的影子
bayes bayesian 贝叶斯定理的
bayesian algorithm 贝叶斯算法
bayesian analysis 贝叶斯分析
bayesian approach bayes氏方法; 贝斯分析法
bayesian classification 贝叶斯分类器
bayesian classifier 贝叶斯分类器
bayesian estimate 贝叶斯估计
bayesian estimation 贝叶斯估计
bayesian formula 式方程式
bayesian inference 贝叶斯定理; 贝叶斯推断; 贝叶斯推理; 贝叶斯推论
bayesian learning 贝式学习
bayesian logic 贝叶斯逻辑
bayesian models 贝式模型法
bayesian networks 贝叶斯网络
bayesian probability 贝叶斯概率
bayesian regression 贝叶斯回归
bayesian statistic 贝叶斯统计
bayesian statistics 贝式统计学; 贝叶斯统计:一种尚有某些争论的统计方法学

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例句与用法

The often-used classification is classification by decision tree induction, bayesian classification and bayesian belief networks, k-nearest neighbor classifiers, rough set theory and fuzzy set approaches
分类算法常见的有判定树归纳分类、贝叶斯分类和贝叶斯网络、k-最临近分类、粗糙集方法以及模糊集方法。
A learning algorithm of compressed candidates based on bayesia belief network is developed to solve slow running problem of traditional bayesian belief network constructing algorithm
摘要针对传统算法分类速度较慢的不足，改进传统算法中候选变量的搜索方式，提出用依赖度量函数测量变量之间的依赖程度，得出压缩候选的贝叶斯信念网络构造算法。
There are many techniques for data classification such as decision tree induction, bayesian classification and bayesian belief networks, association-based classification, genetic algorithms, rough sets, and k-nearest neighbor classifiers
挖掘分类模式的方法有多种，如决策树方法、贝叶斯网络、遗传算法、基于关联的分类方法、粗糙集和k-最临近方法等等。
With the rapid development of computer network and communication technology, a great deal of information which comes from different domains is comprehended and accepted by people via computers . and along with the higher demand the captured information and the communication exchanges more frequently, the information semantic integration is needed in computers ’ sharing data . uncertainty knowledge becomes more prevalent in many application . probability theory has been proved to be one of the most powerful approaches to capture the degree of belief about uncertain knowledge . 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 . ontology mapping has widely relied on the ontology semantic integration
随着计算机网络和信息技术的高速发展,不同知识领域的大量信息需要人们通过计算机来理解和接受,而且随着人们对信息获取的要求越来越高,以及信息间数据交换的日益频繁,需要对计算机之间的信息交换进行语义级的合成。不确定性知识也得到了越来越普遍的应用和发展。概率理论已经被证明是获得非确定性知识的置信度的最有力方法之一。

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Last modified time:Mon, 11 Aug 2025 00:29:56 GMT