图 书 馆
随机图表GP回归的协方差函数和贝叶斯误差Covariance functions and Bayes errors for GP regression on random graphs |
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| 课程网址: | http://videolectures.net/bark08_sollich_cfabefgr/ |
| 主讲教师: | Peter Sollich |
| 开课单位: | 伦敦国王学院 |
| 开课时间: | 2008-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We consider GP learning of functions defined on the nodes of a random graph. Covariance functions proposed for this scenario, based on diffusion processes on the graph, are shown to have some counter-intuitive properties. In particular, on graphs with tree-like structure where loops can be neglected (as is typically the case for randomly generated graphs), the "obvious" limit of a large correlation length scale does not produce a constant covariance function. In the second part, we look at Bayes errors for GP regression on graphs and study how the learning curves depend on the size of the graph, its connectivity, and the number of training examples. Joint work with Camille Coti. |
| 关 键 词: | 随即图表; 函数; 贝叶斯误差 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-17：lxf |
| 阅读次数: | 47 |