图 书 馆
基于高斯过程的多任务学习Multi-task Learning with Gaussian Processes |
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| 课程网址: | http://videolectures.net/bark08_williams_mtlwgp/ |
| 主讲教师: | Chris Williams |
| 开课单位: | 爱丁堡大学 |
| 开课时间: | 2008-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We consider the problem of multi-task learning, i.e. the setup where there are multiple related prediction problems (tasks), and we seek to improve predictive performance by sharing information across the different tasks. We address this problem using Gaussian process (GP) predictors, using a model that learns a shared covariance function on input-dependent features and a ``free-form'' covariance matrix that specifies inter-task similarity. We discuss the application of the method to a number of real-world problems such as compiler performance prediction and learning robot inverse dynamics. Joint work with Kian Ming Chai, Edwin Bonilla, Stefan Klanke, Sethu Vijayakumar (Edinburgh) |
| 关 键 词: | 计算机科学; 机器学习; 高斯过程 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-02-16：nkq |
| 阅读次数: | 128 |