学习主要凸推理 ][Learning Convex Inference of Marginals]

学习主要凸推理 Learning Convex Inference of Marginals


课程网址:	http://videolectures.net/uai08_domke_lci/
主讲教师:	Justin Domke
开课单位:	澳大利亚信息通信技术研究中心
开课时间:	2008-07-30
课程语种:	英语
中文简介:	使用最大似然训练的图形模型是边缘分布概率推断的常用工具。然而，当模型的推理过程是近似的时，这种方法会遇到困难。本文首先将推理过程定义为凸函数的极小化，其灵感来源于自由能近似。然后直接根据单变量边际预测中推理过程的表现进行学习。主要的新颖性在于，这是一种直接最小化的经验风险，其中风险衡量预测保证金的准确性。
课程简介:	Graphical models trained using maximum likelihood are a common tool for probabilistic inference of marginal distributions. However, this approach suffers difficulties when either the inference process of the model is approximate. In this paper, the inference process is first defined to be the minimization of a convex function, inspired by free energy approximations. Learning is then done directly in terms of the performance of the inference process at univariate marginal prediction. The main novelty is that this is a direct minimization of empirical risk, where the risk measures the accuracy of predicted marginals.
关键词:	图形模型; 风险预测; 凸函数
课程来源:	视频讲座网
最后编审:	2021-02-28：nkq
阅读次数:	37

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