图 书 馆
正则化损失最小化的随机双坐标上升法Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization |
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| 课程网址: | http://videolectures.net/nipsworkshops2012_shalev_shwartz_minimiz... |
| 主讲教师: | Shai Shalev-Shwartz |
| 开课单位: | 耶路撒冷希伯来大学 |
| 开课时间: | 2013-01-16 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Stochastic Gradient Descent (SGD) has become popular for solving large scale supervised machine learning optimization problems such as SVM, due to their strong theoretical guarantees. While the closely related Dual Coordinate Ascent (DCA) method has been implemented in various software packages, it has so far lacked good convergence analysis. We present a new analysis of Stochastic Dual Coordinate Ascent (SDCA) showing that this class of methods enjoy strong theoretical guarantees that are comparable or better than SGD. This analysis justifies the effectiveness of SDCA for practical applications. |
| 关 键 词: | 随机梯度下降; 监督机器学习; 双坐标上升 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-08：lxf |
| 阅读次数: | 162 |