图 书 馆
L1正则化损失最小化的随机方法Stochastic Methods for L1 Regularized Loss Minimization |
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| 课程网址: | http://videolectures.net/icml09_tewari_smrl/ |
| 主讲教师: | Ambuj Tewari |
| 开课单位: | 芝加哥丰田技术学院 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We describe and analyze two stochastic methods for $\ell_1$ regularized loss minimization problems, such as the Lasso. The first method updates the weight of a single feature at each iteration while the second method updates the entire weight vector but only uses a single training example at each iteration. In both methods, the choice of feature/example is uniformly at random. Our theoretical runtime analysis suggests that the stochastic methods should outperform state-of-the-art deterministic approaches, including their deterministic counterparts, when the size of the problem is large. We demonstrate the advantage of stochastic methods by experimenting with synthetic and natural data sets. |
| 关 键 词: | 正则化; 权重向量; 随机方法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-24：lxf |
| 阅读次数: | 37 |