图 书 馆
rkhs嵌入方法的贝叶斯解释Bayesian Interpretations of RKHS Embedding Methods |
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| 课程网址: | http://videolectures.net/nipsworkshops2012_duvenaud_bayesian/ |
| 主讲教师: | David Kristjanson Duvenaud |
| 开课单位: | 多伦多大学 |
| 开课时间: | 2013-01-16 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We give a simple interpretation of mean embeddings as expectations under a Gaussian process prior. Methods such as kernel two-sample tests, the Hilbert-Schmidt Independence Criterion, and kernel herding are all based on distances between mean embeddings, also known as the Maximum Mean Discrepancy (MMD). This Bayesian interpretation allows a derivation of optimal herding weights, principled methods of kernel learning, and sheds light on the assumptions necessary for MMD-based methods to work in practice. In the other direction, the MMD interpretation gives tight, closed-form bounds on the error of Bayesian estimators. |
| 关 键 词: | 高斯过程; 平均嵌入; 贝叶斯解释 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-08：lxf |
| 阅读次数: | 75 |