树增强朴素贝叶斯使用截断指数函数的混合物的回归:在高等教育管理中的应用Tree Augmented Naive Bayes for Regression Using Mixtures of Truncated Exponentials: Application to Higher Education Management |
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课程网址: | http://videolectures.net/ida07_salmeron_tanb/ |
主讲教师: | Antonio Salmerón |
开课单位: | 阿尔梅里亚大学 |
开课时间: | 2007-10-08 |
课程语种: | 英语 |
中文简介: | 本文探讨了树增广朴素贝叶斯(TAN)在回归问题中的应用,其中一些自变量是连续的,另一些是离散的。提出的解决方案是基于联合分布的近似截断指数(MTE)的混合物。TAN结构的构造需要使用条件互信息,而条件互信息无法通过分析获得MTE。为了解决这个问题,我们引入了一种基于蒙特卡罗估计的条件互信息无偏估计。我们在与高等教育管理相关的现实环境中测试了该模型的性能,在这种环境中,离散变量和连续变量的回归问题很常见。这项工作得到了西班牙教育和科学部项目Tin2004-06204-C03-01和Junta de Andalucí;A、项目P05-TIC-00276的支持。 |
课程简介: | In this paper we explore the use of Tree Augmented Naive Bayes (TAN) in regression problems where some of the independent variables are continuous and some others are discrete. The proposed solution is based on the approximation of the joint distribution by a Mixture of Truncated Exponentials (MTE). The construction of the TAN structure requires the use of the conditional mutual information, which cannot be analytically obtained for MTEs. In order to solve this problem, we introduce an unbiased estimator of the conditional mutual information, based on Monte Carlo estimation. We test the performance of the proposed model in a real life context, related to higher education management, where regression problems with discrete and continuous variables are common. This work has been supported by the Spanish Ministry of Education and Science, project TIN2004-06204-C03-01 and by Junta de Andalucía, project P05-TIC-00276. |
关 键 词: | 贝叶斯学习 ; 计算机科学; 机器学习; 回归 |
课程来源: | 视频讲座网 |
最后编审: | 2019-11-23:lxf |
阅读次数: | 56 |