图 书 馆
大规模经验风险最小化的现代凸优化方法Modern Convex Optimization Methods for Large-scale Empirical Risk Minimization |
|
| 课程网址: | http://videolectures.net/icml2015_schmidt_risk_minimization/ |
| 主讲教师: | Mark Schmidt |
| 开课单位: | 不列颠哥伦比亚大学计算机科学系 |
| 开课时间: | 2015-12-05 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This tutorial reviews recent advances in convex optimization for training (linear) predictors via (regularized) empirical risk minimization. We exclusively focus on practically efficient methods which are also equipped with complexity bounds confirming the suitability of the algorithms for solving huge-dimensional problems (a very large number of examples or a very large number of features). The first part of the tutorial is dedicated to modern primal methods (belonging to the stochastic gradient descent variety), while the second part focuses on modern dual methods (belonging to the randomized coordinate ascent variety). While we make this distinction, there are very close links between the primal and dual methods, some of which will be highlighted. We shall also comment on mini-batch, parallel and distributed variants of the methods as this is an important consideration for applications involving big data. |
| 关 键 词: | 经验风险; 复杂界限; 随机梯度; 对偶方法 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-04-24：chenxin01 |
| 最后编审: | 2023-05-18：chenxin01 |
| 阅读次数: | 23 |