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可以帮助学习内核的性能？Can Learning Kernels Help Performance? |
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| 课程网址: | http://videolectures.net/icml09_cortes_clkh/ |
| 主讲教师: | Corinna Cortes |
| 开课单位: | 谷歌公司 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Kernel methods combined with large-margin learning algorithms such as SVMs have been used successfully to tackle a variety of learning tasks since their introduction in the early 90s. However, in the standard framework of these methods, the choice of an appropriate kernel is left to the user and a poor selection may lead to sub-optimal performance. Instead, sample points can be used to select a kernel function suitable for the task out of a family of kernels fixed by the user. While this is an appealing idea supported by some recent theoretical guarantees, in experiments, it has proven surprisingly difficult to consistently and significantly outperform simple fixed combination schemes of kernels. This talk will survey different methods and algorithms for learning kernels and will present novel results that tend to suggest that significant performance improvements can be obtained with a large number of kernels. (Includes joint work with Mehryar Mohri and Afshin Rostamizadeh.) |
| 关 键 词: | 核函数的选择; 样本点; 内核 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-08：吴雨秋(课程编辑志愿者) |
| 阅读次数: | 36 |