图 书 馆
高效的在线多类预测土匪算法Efficient Bandit Algorithms for Online Multiclass Prediction |
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| 课程网址: | http://videolectures.net/icml08_shwartz_eba/ |
| 主讲教师: | Shai Shalev-Shwartz |
| 开课单位: | 耶路撒冷希伯来大学 |
| 开课时间: | 2008-08-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This paper introduces the Banditron, a variant of the Perceptron, for the multiclass bandit setting. The multiclass bandit setting models a wide range of practical supervised learning applications where the learner only receives partial feedback (referred to as "bandit" feedback, in the spirit of multi-armed bandit models) with respect to the true label (e.g. in many web applications users often only provide positive "click" feedback which does not necessarily fully disclose a true label). The Banditron has the ability to learn in a multiclass classification setting with the "bandit" feedback which only reveals whether or not the prediction made by the algorithm was correct or not (but does not necessarily reveal the true label). We provide (relative) mistake bounds which show how the Banditron enjoys favorable performance, and our experiments demonstrate the practicality of the algorithm. Furthermore, this paper pays close attention to the important special case when the data is linearly separable --- a problem which has been exhaustively studied in the full information setting yet is novel in the bandit setting. |
| 关 键 词: | 多类强盗设置模型; 良好性能; 实用性 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-05-21：王淑红(课程编辑志愿者) |
| 阅读次数: | 125 |