图 书 馆
稀疏性遗化在线性回归中对单个序列的界限Sparsity regret bounds for individual sequences in online linear regression |
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| 课程网址: | http://videolectures.net/colt2011_gerchinovitz_linear/ |
| 主讲教师: | Sébastien Gerchinovitz |
| 开课单位: | 巴黎高等师范学校 |
| 开课时间: | 2011-08-02 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We consider the problem of online linear regression on arbitrary deterministic sequences when the ambient dimension d can be much larger than the number of time rounds T. We introduce the notion of sparsity regret bound, which is a deterministic online counterpart of recent risk bounds derived in the stochastic setting under a sparsity scenario. We prove such regret bounds for an online-learning algorithm called SeqSEW and based on exponential weighting and data-driven truncation. In a second part we apply a parameter-free version of this algorithm on i.i.d. data and derive risk bounds of the same flavor as in Dalalyan and Tsybakov (2008, 2011) but which solve two questions left open therein. In particular our risk bounds are adaptive (up to a logarithmic factor) to the unknown variance of the noise if the latter is Gaussian. |
| 关 键 词: | 序列; 线性回归; 算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-02-23：chenxin |
| 阅读次数: | 82 |