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分层最大熵密度估计Hierarchical Maximum Entropy Density Estimation |
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| 课程网址: | http://videolectures.net/icml07_dudik_hmed/ |
| 主讲教师: | Miroslav Dudík |
| 开课单位: | 普林斯顿大学 |
| 开课时间: | 2007-06-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We study the problem of simultaneously estimating several densities where the datasets are organized into overlapping groups, such as a hierarchy. For this problem, we propose a maximum entropy formulation, which systematically incorporates the groups and allows us to share the strength of prediction across similar datasets. We derive general performance guarantees, and show how some previous approaches, such as hierarchical shrinkage and hierarchical priors, can be derived as special cases. We demonstrate the proposed technique on synthetic data and in a realworld application to modeling the geographic distributions of species hierarchically grouped in a taxonomy. Specifically, we model the geographic distributions of species in the Australian wet tropics and Northeast New South Wales. In these regions, small numbers of samples per species significantly hinder effective prediction. Substantial benefits are obtained by combining information across taxonomic groups. |
| 关 键 词: | 最大熵公式; 共享预测; 跨分类群 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-17：lxf |
| 阅读次数: | 89 |