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概率数据库中的参数学习:最小二乘法

Parameter Learning in Probabilistic Databases: A Least Squares Approach
课程网址: http://videolectures.net/ecmlpkdd08_gutmann_plip/  
主讲教师: Bernd Gutmann, Kristian Kersting, Luc De Raedt, Angelika Kimmig
开课单位: 弗莱堡大学
开课时间: 2008-10-10
课程语种: 英语
中文简介:
介绍了概率数据库problog的参数学习问题。考虑到一组查询的观察到的成功概率,我们计算了附加在训练示例和未知示例上具有较低近似误差的事实上的概率。假设高斯误差项对观测到的成功概率,这自然会导致最小二乘优化问题。我们的方法称为leproblog,能够从查询和证明中学习,甚至可以同时从两者中学习。这使得它变得灵活,并允许在有证据的领域进行更快的培训。对实际数据的实验表明,这种最小二乘法校准概率数据库的有效性和实用性。
课程简介: We introduce the problem of learning the parameters of the probabilistic database ProbLog. Given the observed success probabilities of a set of queries, we compute the probabilities attached to facts that have a low approximation error on the training examples as well as on unseen examples. Assuming Gaussian error terms on the observed success probabilities, this naturally leads to a least squares optimization problem. Our approach, called LeProbLog, is able to learn both from queries and from proofs and even from both simultaneously. This makes it flexible and allows faster training in domains where the proofs are available. Experiments on real world data show the usefulness and effectiveness of this least squares calibration of probabilistic databases.
关 键 词: 计算机科学; 机器学习; 统计学习
课程来源: 视频讲座网
最后编审: 2019-12-06:lxf
阅读次数: 24