图 书 馆
多任务学习促进与应用程序的网络搜索排名Multi-Task Learning for Boosting with Application to Web Search Ranking |
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| 课程网址: | http://videolectures.net/kdd2010_vadrevu_mtlbaw/ |
| 主讲教师: | Srinivas Vadrevu |
| 开课单位: | 雅虎硅谷研究院 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing the specifics of each learning task with task-specific parameters and the commonalities between them through shared parameters. This enables implicit data sharing and regularization. We evaluate our learning method on web-search ranking data sets from several countries. Here, multitask learning is particularly helpful as data sets from different countries vary largely in size because of the cost of editorial judgments. Our experiments validate that learning various tasks jointly can lead to significant improvements in performance with surprising reliability. |
| 关 键 词: | 决策树; 算法; 隐式数据; 多任务学习 |
| 课程来源: | 视频讲座网公开课 |
| 最后编审: | 2020-05-21：王淑红(课程编辑志愿者) |
| 阅读次数: | 119 |