图 书 馆
将主动学习推向最优：可追踪蒙特卡罗和台球算法Boosting Active Learning to Optimality: a Tracable Monte-Carlo, Billiard-Based Algorithm |
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| 课程网址: | http://videolectures.net/ecmlpkdd09_rolet_balo/ |
| 主讲教师: | Philippe Rolet |
| 开课单位: | 巴黎第十一大学 |
| 开课时间: | 2009-10-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This paper focuses on Active Learning with a limited number of queries; in application domains such as Numerical Engineering, the size of the training set might be limited to a few dozen or hundred examples due to computational constraints. Active Learning under bounded resources is formalized as a finite horizon Reinforcement Learning problem, where the sampling strategy aims at minimizing the expectation of the generalization error. A tractable approximation of the optimal (intractable) policy is presented, the Bandit-based Active Learner (Baal) algorithm. Viewing Active Learning as a single-player game, Baal combines UCT, the tree structured multi-armed bandit algorithm proposed by Kocsis and Szepesvari (2006), and billiard algorithms. A proof of principle of the approach demonstrates its good empirical convergence toward an optimal policy and its ability to incorporate prior AL criteria. Its hybridization with the Query-by-Committee approach is found to improve on both stand-alone Baal and stand-alone QbC. |
| 关 键 词: | 数值工程; 计算约束; 有限时间强化学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-03-27：lxf |
| 阅读次数: | 61 |