图 书 馆
核回归的核心集Coresets for Kernel Regression |
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| 课程网址: | http://videolectures.net/kdd2017_zheng_kernel_regression/ |
| 主讲教师: | Yan Zheng |
| 开课单位: | 犹他大学 |
| 开课时间: | 2017-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Kernel regression is an essential and ubiquitous tool for non-parametric data analysis, particularly popular among time series and spatial data. However, the central operation which is performed many times, evaluating a kernel on the data set, takes linear time. This is impractical for modern large data sets. In this paper we describe coresets for kernel regression: compressed data sets which can be used as proxy for the original data and have provably bounded worst case error. The size of the coresets are independent of the raw number of data points, rather they only depend on the error guarantee, and in some cases the size of domain and amount of smoothing. We evaluate our methods on very large time series and spatial data, and demonstrate that they incur negligible error, can be constructed extremely efficiently, and allow for great computational gains. |
| 关 键 词: | 核回归; 中央操作; 压缩数据集 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-12-25：wujk |
| 最后编审: | 2023-12-25：wujk |
| 阅读次数: | 15 |