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   bayesian学习 的翻译结果: 查询用时:0.017秒
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bayesian学习
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  bayesian learning
     A SEMANTIC DESCRIPTION MODEL OF LUNG CANCER CHROMATIC IMAGES BASED ON BAYESIAN LEARNING
     一种基于Bayesian学习的彩色肺癌图像语义描述模型
短句来源
     A Negotiation Model Based on Bayesian Learning
     一个基于Bayesian学习的协商模型
短句来源
     This paper introduces a negotiation model based on Bayesian learning, called NMBL. Agent gets information of the negotiation opponents in every iteration by means of Bayesian learning, updates the pri- or knowledge of the negotiation opponents and then brings forward the offer of the next iteration according to negotia- tion strategies based on the conflicting point and un-compromising degree.
     本文提出了一个基于Bayesian学习的协商模型NMBL:在每一轮协商中,Agent通过Bayesian学习获取协商对手的信息,更新对协商对手的信念,然后根据基于冲突点和不妥协度的协商策略提出下一轮的协商提议。
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  “bayesian学习”译为未确定词的双语例句
     Study of MAS Learning Model Based on the Bayesian Process of the Single Agent
     基于单Agent的Bayesian学习过程的多Agent系统学习模型研究
短句来源
     By introducing the Bayesian framework to the lung cancer diagnosing problem,a semantic description model of lung cancer chromatic images is proposed, which is composed of raw image layer (RIL), image feature layer (IFL), semantic knowledge layer (SKL), and a semantic description algorithm SDA.
     通过将 Bayesian理论框架引入肺癌分类识别问题 ,提出一种基于 Bayesian学习理论的彩色肺癌图像语义描述模型 . 该模型由原始图像层 (raw im age layer,RIL )、图像特征层 (image feature layer,IFL )、语义知识层(sem antic knowledge layer,SKL )以及语义描述算法 SDA构成 .
短句来源
     Three methods that break the stability of naive Bayesian classifier are given.
     提出了3种破坏Naive Bayesian学习器稳定性的方法。
短句来源
     A new Bayesian based model for user interests learning has been proposed.
     提出一个基于 Bayesian学习的用户兴趣模型 ,用随机变量来刻画用户在图像检索过程中的个性倾向 .
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  相似匹配句对
     Learning with a Bayesian Networks a Set of Conditional Probility Tables
     Bayesian网中概率参数学习方法
短句来源
     A Negotiation Model Based on Bayesian Learning
     一个基于Bayesian学习的协商模型
短句来源
     Learning of Organism
     有机体的学习
短句来源
     LEARNING SHOULD BE FUN
     快乐学习
短句来源
     The Independence Relations in Bayesian Networks
     Bayesian网中的独立关系
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  bayesian learning
The approach can be used to speed up several well-known learning methods such as variational Bayesian learning (ensemble learning) and expectation-maximization algorithm with modest algorithmic modifications.
      
Experimental results show that the proposed method is able to reduce the required convergence time by 60-85% in realistic variational Bayesian learning problems.
      
Bayesian learning leads to correlated equilibria in normal form games
      
We formulate an infinite-horizon Bayesian learning model in which the planner faces a cost from switching actions that does not approach zero as the size of the change vanishes.
      
Bayesian learning and convergence to Nash equilibria without common priors
      
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By introducing the Bayesian framework to the lung cancer diagnosing problem,a semantic description model of lung cancer chromatic images is proposed, which is composed of raw image layer (RIL), image feature layer (IFL), semantic knowledge layer (SKL), and a semantic description algorithm SDA. Based on this model, a lung cancer identification system is successfully implemented.The experiment results also show that this model is very effective to identify different kinds of lung cancer at high correct rates.

通过将 Bayesian理论框架引入肺癌分类识别问题 ,提出一种基于 Bayesian学习理论的彩色肺癌图像语义描述模型 .该模型由原始图像层 (raw im age layer,RIL )、图像特征层 (image feature layer,IFL )、语义知识层(sem antic knowledge layer,SKL )以及语义描述算法 SDA构成 .基于此模型提出一种肺癌分类识别算法 ,并实现了一个肺癌分类识别系统 .实验表明 ,该模型具有较高的肺癌分类准确率 ,是行之有效的

A new Bayesian based model for user interests learning has been proposed.We introduce stochastic variables to describe user's individual characteristics, and analyze the image retrieval effects while the DFs(distribution functions) of stochastic variables is changing in some classes.The experiments show that the retrieval effects is better using the dynamic linear DFs than fixed normal DFs and uniform DFs.

提出一个基于 Bayesian学习的用户兴趣模型 ,用随机变量来刻画用户在图像检索过程中的个性倾向 .重点分析了在随机变量的分布函数是动态变化的一族线性函数时 ,模型对检索效果的影响 .实验结果显示 ,利用线性族的动态分布函数比固定正态分布函数以及均匀分布函数会获得更好的检索效果

Naive Bayesian classifier is a kind of effective text categorization methods,but it is hard to improve its performance by Boosting procedure because of its stability.So the main problem derived from the Boosting procedure using Naive Bayesian classifier as the basic classifier is how to break its stability.Three methods that break the stability of naive Bayesian classifier are given.The first method changes samples of the training set,the second adopts the random selected feature group,and the third creates...

Naive Bayesian classifier is a kind of effective text categorization methods,but it is hard to improve its performance by Boosting procedure because of its stability.So the main problem derived from the Boosting procedure using Naive Bayesian classifier as the basic classifier is how to break its stability.Three methods that break the stability of naive Bayesian classifier are given.The first method changes samples of the training set,the second adopts the random selected feature group,and the third creates different feature set using different method to extract text features in each iteration of Boosting procedure.The three methods have respective advantages and disadvantages,but all of them are more accurate and effective than the original Naive Bayesian classifier.

Naive Bayesian分类器是一种有效的文本分类方法,但由于具有较强的稳定性,很难通过Boosting机制提高其性能。因此用Naive Bayesian分类器作为Boosting的基分类器需要解决的最大问题,就是如何破坏Naive Bayesian分类器的稳定性。提出了3种破坏Naive Bayesian学习器稳定性的方法。第一种方法改变训练集样本,第二种方法采用随机属性选择社团,第三种方法是在Boosting的每次迭代中利用不同的文本特征提取方法建立不同的特征词集。实验表明,这几种方法各有其优缺点,但都比原有方法准确、高效。

 
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