图 书 馆
用许多可复制内核希尔伯特空间来辅助学习Learning with Many Reproducing Kernel Hilbert Spaces |
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| 课程网址: | http://videolectures.net/smls09_yuan_lwmrk/ |
| 主讲教师: | Ming Yuan |
| 开课单位: | 乔治亚理工学院 |
| 开课时间: | 2009-05-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this talk, we consider the problem of learning a target function that belongs to the linear span of a large number of reproducing kernel Hilbert spaces. Such a problem arises naturally in many practice situations with the ANOVA, the additive model and multiple kernel learning as the most well known and important examples. We investigate approaches based on l1-type complexity regularization and the nonnegative garrote respectively. We show that the computation of both procedures can be done efficiently and the nonnegative garrote could be more favorable at times. We also study their theoretical properties from both variable selection and estimation perspective. We establish several probabilistic inequalities providing bounds on the excess risk and L2-error that depend on the sparsity of the problem. Part of the talk is based on joint work with Vladimir Koltchinskii. |
| 关 键 词: | 学习目标函数; 可再生内核; 线性跨度 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-21：cwx |
| 阅读次数: | 67 |