支持向量机的鲁棒性与正则化Robustness and Regularization of Support Vector Machines |
|
课程网址: | http://videolectures.net/opt08_xu_raros/ |
主讲教师: | Huan Xu |
开课单位: | 新加坡国立大学 |
开课时间: | 2008-12-20 |
课程语种: | 英语 |
中文简介: | 我们考虑一个强大的分类问题,并表明标准正则化SVM是我们公式的一个特例,在规则化和鲁棒性之间提供了明确的联系。同时,噪声和鲁棒性的物理连接表明了广泛的新系列强大的分类算法的潜力。最后,我们通过仅使用鲁棒性参数(而不是VC维度或稳定性)来证明支持向量机的一致性,表明鲁棒性是分类算法的基本属性。 |
课程简介: | We consider a robust classification problem and show that standard regularized SVM is a special case of our formulation, providing an explicit link between reg- ularization and robustness. At the same time, the physical connection of noise and robustness suggests the potential for a broad new family of robust classification algorithms. Finally, we show that robustness is a fundamental property of classi- fication algorithms, by re-proving consistency of support vector machines using only robustness arguments (instead of VC dimension or stability). |
关 键 词: | 标准正则化; 规则化; 鲁棒性 |
课程来源: | 视频讲座网 |
最后编审: | 2019-09-12:lxf |
阅读次数: | 34 |