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Steppest descent analysis for unregularized linear prediction with strictly convex penaltiesSteppest descent analysis for unregularized linear prediction with strictly convex penalties |
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| 课程网址: | http://videolectures.net/nipsworkshops2011_telgarsky_penalties/ |
| 主讲教师: | Matus Telgarsky |
| 开课单位: | 加州大学圣地亚哥分校 |
| 开课时间: | 2012-01-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This manuscript presents a convergence analysis, generalized from a study of boosting, of unregularized linear prediction. Here the empirical risk — incorporating strictly convex penalties composed with a linear term — may fail to be strongly convex, or even attain a minimizer. This analysis is demonstrated on linear regression, decomposable objectives, and boosting. |
| 关 键 词: | 收敛性分析; 非均匀化线性预测; 回归 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-07：lxf |
| 阅读次数: | 35 |