图 书 馆
卷积多输出高斯过程的先验知识与稀疏方法Prior Knowledge and Sparse Methods for Convolved Multiple Outputs Gaussian Processes |
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| 课程网址: | http://videolectures.net/nipsworkshops09_alvarez_pksmcmogp/ |
| 主讲教师: | Mauricio Alvarez |
| 开课单位: | 曼彻斯特大学 |
| 开课时间: | 2010-01-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | One approach to account for non-trivial correlations between outputs employs convolution processes. Under a latent function interpretation of the convolution transform it is possible to establish dependencies between output variables. Two important aspects in this framework are how can we introduce prior knowledge and how can we perform efficient inference. Relating the convolution operation with dynamical systems, we can specify richer covariance functions for multiple outputs. We also present different sparse approximations for dependent output Gaussian processes in the context of structured covariances. Joint work with Neil Lawrence, David Luengo and Michalis Titsias. |
| 关 键 词: | 卷积过程; 潜在函数; 动态系统 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-07：lxf |
| 阅读次数: | 78 |