图 书 馆
Nu支持向量机作为条件风险最小化Nu-Support Vector Machine as Conditional Value-at-Risk Minimization |
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| 课程网址: | http://videolectures.net/icml08_takeda_nsvm/ |
| 主讲教师: | Akiko Takeda |
| 开课单位: | 庆应义塾大学 |
| 开课时间: | 2008-07-28 |
| 课程语种: | 日语 |
| 中文简介: |  |
| 课程简介: | The nu-support vector classification (nu-SVC) algorithm was shown to work well and provide intuitive interpretations, e.g., the parameter nu roughly specifies the fraction of support vectors. Although nu corresponds to a fraction, it cannot take the entire range between 0 and 1 in its original form. This problem was settled by a non-convex extension of nu-SVC and the extended method was experimentally shown to generalize better than original nu-SVC. However, its good generalization performance and convergence properties of the optimization algorithm have not been studied yet. In this paper, we provide new theoretical insights into these issues and propose a novel nu-SVC algorithm that has guaranteed generalization performance and convergence properties |
| 关 键 词: | 向量分类算法; 优化算法; 泛化性能 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-21：lxf |
| 阅读次数: | 103 |