图 书 馆
漂流游戏，促进和在线学习Drifting Games, Boosting and Online Learning |
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| 课程网址: | http://videolectures.net/mlss09us_freund_dgbol/ |
| 主讲教师: | Yoav Freund |
| 开课单位: | 加州大学圣地亚哥分校 |
| 开课时间: | 2009-07-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Drifting games provide a new and useful framework for analyzing learning algorithms. In this talk I will present the framework and show how it is used to derive a new boosting algorithm, called RobustBoost and a new online prediction algorithm, called NormalHedge. I will present two sets of experiments using these algorithms on synthetic and real world data. The first set demonstrates that RobustBoost can learn from mislabeled training data. The second demonstrating an application of NormalHedge to the tracking moving objects. |
| 关 键 词: | 分析学习算法; 算法的合成; 真实世界的数据; 在线预测算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-10-22：chenxin |
| 阅读次数: | 48 |