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bayesian learning
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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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A general Bayesian framework for obtaining sparse solutions to the regression and classification tasks utilizing models linear in the parameters is presented. By using a probabilistic Bayesian learning framework an accurate RVM (relevance vector machine) prediction models using fewer basis functions than a comparable SVM (support vector machine) and offering a number of additional advantages is obtained. The generalization ability of the current ID (intrusion detection system) is poor with less priori...

A general Bayesian framework for obtaining sparse solutions to the regression and classification tasks utilizing models linear in the parameters is presented. By using a probabilistic Bayesian learning framework an accurate RVM (relevance vector machine) prediction models using fewer basis functions than a comparable SVM (support vector machine) and offering a number of additional advantages is obtained. The generalization ability of the current ID (intrusion detection system) is poor with less priori knowledge. Using RVM in the intrusion detection the generalization ability of the IDS is good when the sample size is small.

介绍一种稀疏的贝叶斯学习算法———关联向量机(RVM),它在再生核希尔伯特空间中学习,利用贝叶斯方法推理,推广能力好,与支持向量机相比不仅解更为稀疏而且不需要调整超参数。应用RVM的对小样本的良好分类能力,提出一种基于RVM的入侵检测原型系统。

As online trading becomes more common,how to apply the advanced Agent technique into automated e-negotiation has beena mainstream in both economics and computer science domain.In this paper, we stated the viewpoints from the respective of the buy-er agent firstly.In negotiation process,we used Bayesian learning to update initial belief of the opponent through the online learningand made each agent to adapt action through learning.Sequentially we could shorten negotiation time and advance negotiation...

As online trading becomes more common,how to apply the advanced Agent technique into automated e-negotiation has beena mainstream in both economics and computer science domain.In this paper, we stated the viewpoints from the respective of the buy-er agent firstly.In negotiation process,we used Bayesian learning to update initial belief of the opponent through the online learningand made each agent to adapt action through learning.Sequentially we could shorten negotiation time and advance negotiation effi-ciency. Moreover,we testified the feasibility by experiment.

随着在线交易越来越普遍,如何有效地将先进的agent技术运用于电子商务协商领域,已经成为经济学家和计算机学者共同研讨的一个主要方向。文中从买方agent的观点出发,在协商过程中采用贝叶斯学习机制进行预测和更新对方agent的信念,使得每个agent通过学习来协调自身的行为,从而缩短了协商时间,提高了协商效率,更能有效地完成协商目的。并实验说明了其可行性。

 
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