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bayesian learning
相关语句
  贝叶斯学习
    Research on Bayesian Learning Theory and Its Application
    贝叶斯学习理论及其应用研究
短句来源
    Combining sparse Bayesian learning with the principle of the support vector tracking(SVT), the relevance vector tracking (RVT) is presented.
    结合稀疏贝叶斯学习方法和支持向量跟踪(SVT)原理,提出了相关向量跟踪(RVT)。
    Boosting Naive Bayesian Learning
    增强型朴素贝叶斯学习
短句来源
    Urban Traffic Multi-agent System Based on RMM and Bayesian Learning
    基于RMM和贝叶斯学习的城市交通多智能体系统
短句来源
    Bayesian learning and inference algorithm based on Bayesian Networks Toolbox
    基于贝叶斯网络工具箱的贝叶斯学习和推理
短句来源
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  bayesian学习
    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。
短句来源
  “bayesian learning”译为未确定词的双语例句
    A Bayesian Learning Algorithm Based on Search-Coding Method
    基于搜索编码的简单贝叶斯分类方法
短句来源
    Novel integrated algorithm of modular neural network's sub-nets based on Bayesian learning
    模块化神经网络的Bayes子网集结新算法研究
短句来源
    MCMC can be used to explore the posterior probability produced by the Bayesian learning method.
    当使用Bayes方法学习Bayes网络模型的结构和参数时,使用MCMC方法对后验分布进行抽样研究是非常方便的,它不需要此概率分布是归一化的,而计算归一化常数往往是困难的。
短句来源
    Aiming at the important issue of modular neural network(MNN)— the dynamic integration of the sub-nets,a novel integrated algorithm based on the improved Bayesian learning is presented.
    针对模块化神经网络的重要命题——子网动态集成问题,提出一种基于改进的Bayes学习的子网集结新方法.
短句来源
    Based on Bayesian learning,a sparse probabilistic model,termed as `relevance vector(machine′(RVM),) is introduced,which has the same functional form as support vector machine(SVM). RVM has an excellent ability in dealing with functional regression with noise. Compared with SVM,RVM has sparser solutions.
    介绍了一种与支持向量机(SVM)函数形式相同的稀疏概率模型——关联向量机(RVM),其训练是在贝叶斯框架下进行的,在处理具有噪声的函数回归时,RVM具有很出色的性能。
短句来源
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  bayesian learning
A system-based decision logic predicated on subjective and objective probabilities is developed incorporating the Bayesian learning process.
      
Besides allowing for exact Bayesian learning, these results permit us to formulate a new class of tractable latent variable models in which the likelihood of a data point is computed through an ensemble average over tree structures.
      
Tractable Bayesian learning of tree belief networks
      
This paper uses a Bayesian learning model to assess the respective influence of different risk measurements on mortality risk perceptions.
      
Also, the results suggest that the determinants of risk perception are consistent with the predictions of a Bayesian learning framework.
      
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This research addressed an urban traffic intelligent control system,which adopts a multi agents coordination in urban traffic control to coordinate the signal of adjacent intersections for eliminating the congestion of traffic network.An agent represents a signal intersection control,and multi agents realize coordination of multiple intersections to eliminate congestion.Based on Recursive Modeling Method and Bayesian learning that enables an agent to select his rational action by examining with other...

This research addressed an urban traffic intelligent control system,which adopts a multi agents coordination in urban traffic control to coordinate the signal of adjacent intersections for eliminating the congestion of traffic network.An agent represents a signal intersection control,and multi agents realize coordination of multiple intersections to eliminate congestion.Based on Recursive Modeling Method and Bayesian learning that enables an agent to select his rational action by examining with other agents by modeling their decision making in conjunction with dynamic belief update.Based on this method,a simplified multi agent traffic control system is established and the results demonstrate its effectiveness.It is very important for ITS.

本文中提出一种城市交通智能控制系统 ,针对城市交通网络中相邻交叉口的交通流可能相互冲突 ,即局部交通流的优化可能引起其他区域交通状况的恶化的问题 ,采用多智能体协调控制方法来协调相邻交叉口处的控制信号消除网络中的交通拥塞 .提出以一个智能体的方式实现一个信号灯交叉口控制 ,对多个信号灯交叉口形成的交通网络采用多智能体协调控制的方式实现网络流量优化来消除拥塞 .文中提出由递归建模和改进的贝叶斯学习相结合的多智能体系统来使智能体可以确定其他智能体的准确模型并实时更新信息 ,并基于上述方法在简单的交通网络模型上建立了多智能体交通控制系统 ,仿真结果表明了方法的有效性 ,对实现智能交通系统有重要意义 .

A multi agent coordination is addressed in urban traffic control, which uses the recursive modeling method(RMM) that enables an agent to select its[KG2/3]rational[KG2/3]action[KG2/3]by[KG2/3]examining[KG1/3]with[KG1/3]other agents by modeling their decision making in a distributed multi agent environment. Bayesian learning is used in conjunction with RMM for belief update. Based on this method, a multi agent traffic control system is established and the results demonstrated its effective.

提出一种基于递归建模方法 (RMM)的多智能体协调方法 ,使智能体在分布式环境下对其它智能体的决策建模选择合理的行动。对 RMM中的信念更新采用贝叶斯学习方法 ,使智能体可以确定其它智能体的准确模型并实时更新信息。在城市交通控制领域建立多智能体交通控制系统 ,仿真结果表明了该方法的有效性 ,对实现智能交通系统具有重要意义。

Classification has been considered as a hot research area in machine learning, pattern recognition and data mining. Incremental learning is an effective method for learning the classification knowledge from massive data, especially in the situation of high cost in getting labeled training examples. Firstly, this paper discusses the difference between Bayesian estimation and classical parameter estimation and denotes the fundamental principle for incorporating the prior knowledge in Bayesian...

Classification has been considered as a hot research area in machine learning, pattern recognition and data mining. Incremental learning is an effective method for learning the classification knowledge from massive data, especially in the situation of high cost in getting labeled training examples. Firstly, this paper discusses the difference between Bayesian estimation and classical parameter estimation and denotes the fundamental principle for incorporating the prior knowledge in Bayesian learning. Then we provide the incremental Bayesian learning model. This model explains the Bayesian learning process that changes the belief with the prior knowledge and new examples information. By selecting the Dirichlet prior distribution, we show this process in detail. In the second session, we mainly discuss the incremental process. For new examples for incremental learning, there exist two statuses: with labels and without labels. As for examples with labels, it is easy to update the classification parameter with the help of conjunct Dirichlet distribution. So it is the key point to learn from unlabeled examples. Different from the method provided by Kamal Nigam, which learns from unlabeled examples using EM algorithm, we focus on the next example that would be selected in learning. This paper gives a method measuring the classification loss with 0 1 loss. We will select the examples that minimize the classification loss. Meanwhile, to improve the algorithm performance, the pool based technique is introduced. For each turn, we only compute the classification loss for examples in pool. Because the basic operations in learning are updating the classification parameters and classifying test instances incrementally, we give their approximate expressions. For testing algorithm's efficiency, this paper makes an experiment on mushroom data set in UCI repository. The initial training set contains 6 labeled examples. Then several unlabeled examples are added. The final experimental results show that this algorithm is feasible and effective.

分类一直是机器学习、模式识别和数据挖掘研究的核心问题 .从海量数据中学习分类知识 ,尤其是当获得大量的带有类别标注的样本代价较高时 ,增量学习是解决该问题的有效途径 .该文将简单贝叶斯方法应用于增量分类中 ,提出了一种增量贝叶斯学习模型 ,给出了增量贝叶斯推理过程 ,包括增量地修正分类器参数和增量地分类测试样本 .实验结果表明 ,该算法是可行的和有效的

 
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