图 书 馆
内核分位数回归的交叉验证解的双层路径跟踪Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression |
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| 课程网址: | http://videolectures.net/icml08_rosset_bilp/ |
| 主讲教师: | Saharon Rosset |
| 开课单位: | 特拉维夫大学 |
| 开课时间: | 2008-07-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Modeling of conditional quantiles requires specification of the quantile being estimated and can thus be viewed as a parameterized predictive modeling problem. Quantile loss is typically used, and it is indeed parameterized by a quantile parameter. In this paper we show how to follow the path of cross validated solutions to regularized kernel quantile regression. Even though the bi-level optimization problem we encounter for every quantile is non-convex, the manner in which the optimal cross-validated solution evolves with the parameter of the loss function allows tracking of this solution. We prove this property, construct the resulting algorithm, and demonstrate it on data. This algorithm allows us to efficiently solve the whole family of bi-level problems. |
| 关 键 词: | 条件分位数; 参数化预测建模; 交叉验证解 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-21：lxf |
| 阅读次数: | 73 |