图 书 馆
多任务学习：贝叶斯方法Multitask learning: the Bayesian way |
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| 课程网址: | http://videolectures.net/oh06_heskes_bw/ |
| 主讲教师: | Tom Heskes |
| 开课单位: | 奈梅亨拉德堡大学 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Multi-task learning lends itself particularly well to a Bayesian approach. Cross-inference between tasks can be implemented by sharing parameters in the likelihood model and the prior for the task-specific model parameters. Choosing different priors, one can implement task clustering and task gating. Throughout my presentation, predicting single-copy newspaper sales will serve as a running example. |
| 关 键 词: | 多任务学习; 贝叶斯方法; 任务聚类 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-09：chenjy |
| 最后编审: | 2023-03-09：chenjy |
| 阅读次数: | 21 |