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随机大图中高斯过程回归的精确学习曲线Exact learning curves for Gaussian process regression on large random graphs |
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| 课程网址: | http://videolectures.net/nips2010_urry_elc/ |
| 主讲教师: | Matthew Urry |
| 开课单位: | 伦敦国王学院 |
| 开课时间: | 2011-03-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We study learning curves for Gaussian process regression which characterise performance in terms of the Bayes error averaged over datasets of a given size. Whilst learning curves are in general very difficult to calculate we show that for discrete input domains, where similarity between input points is characterised in terms of a graph, accurate predictions can be obtained. These should in fact become exact for large graphs drawn from a broad range of random graph ensembles with arbitrary degree distributions where each input (node) is connected only to a finite number of others. The method is based on translating the appropriate belief propagation equations to the graph ensemble. We demonstrate the accuracy of the predictions for Poisson (Erdos-Renyi) and regular random graphs, and discuss when and why previous approximations to the learning curve fail. |
| 关 键 词: | 高斯过程; 贝叶斯误差; 曲线 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-01-16：chenxin |
| 阅读次数: | 92 |