图 书 馆
基于边距和半径的多核学习Margin and Radius Based Multiple Kernel Learning |
|
| 课程网址: | http://videolectures.net/ecmlpkdd09_do_mrbmkl/ |
| 主讲教师: | Huyen Thi Thanh Do |
| 开课单位: | 日内瓦大学 |
| 开课时间: | 2009-10-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | A serious drawback of kernel methods, and Support Vector Machines (SVM) in particular, is the difficulty in choosing a suitable kernel function for a given dataset. One of the approaches proposed to address this problem is Multiple Kernel Learning (MKL) in which several kernels are combined adaptively for a given dataset. Many of the existing MKL methods use the SVM objective function and try to find a linear combination of basic kernels such that the separating margin between the classes is maximized. However, these methods ignore the fact that the theoretical error bound depends not only on the margin, but also on the radius of the smallest sphere that contains all the training instances. We present a novel MKL algorithm that optimizes the error bound taking account of both the margin and the radius. The empirical results show that the proposed method compares favorably with other state-of-the-art MKL methods. |
| 关 键 词: | 内核方法; 向量机; 给定数据集 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-03-24：cwx |
| 阅读次数: | 37 |