theoretically optimal parameter choices for support vector regression machines with noisy input
文献类型：期刊论文
| 作者 | Wang ST ; Zhu JG ; Chung FL ; Lin Q ; Hu DW |
| 刊名 | SOFT COMPUTING
 |
| 出版日期 | 2005 |
| 卷号 | 9期号:10页码:732-741 |
| 关键词 | 复合事件,检测regularized linear regression support vectors Huber loss functions norm-r loss functions |
| ISSN号 | 1432-7643 |
| 中文摘要 | 在大规模事件通知服务的通用框架基础上,通过分析提出了复合事件检测的基本模型,并对照该基本模型剖析了复合事件检测的四种基本方法:基于Petri网、基于匹配树、基于图以及基于自动机的检测方法,评价了各种方法的优缺点,为开发适用于新的应用需求的复合事件检测技术打下了基础。 |
| 学科主题 | Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications |
| 收录类别 | SCI ; ACM ; CNKI |
| 语种 | 英语 |
| 公开日期 | 2011-07-28 |
| 附注 | With the evidence framework, the regularized linear regression model can be explained as the corresponding MAP problem in this paper, and the general dependency relationships that the optimal parameters in this model with noisy input should follow is then derived. The support vector regression machines Huber-SVR and Norm-r r-SVR are two typical examples of this model and their optimal parameter choices are paid particular attention. It turns out that with the existence of the typical Gaussian noisy input, the parameter mu in Huber-SVR has the linear dependency with the input noise, and the parameter r in the r-SVR has the inversely proportional to the input noise. The theoretical results here will be helpful for us to apply kernel-based regression techniques effectively in practical applications. |
| 源URL | [http://124.16.136.157/handle/311060/12442]  |
| 专题 | 软件研究所_软件所图书馆_期刊论文 |
| 推荐引用方式 GB/T 7714 | Wang ST,Zhu JG,Chung FL,et al. theoretically optimal parameter choices for support vector regression machines with noisy input[J]. SOFT COMPUTING,2005,9(10):732-741. |
| APA | Wang ST,Zhu JG,Chung FL,Lin Q,&Hu DW.(2005).theoretically optimal parameter choices for support vector regression machines with noisy input.SOFT COMPUTING,9(10),732-741. |
| MLA | Wang ST,et al."theoretically optimal parameter choices for support vector regression machines with noisy input".SOFT COMPUTING 9.10(2005):732-741. |
入库方式： OAI收割
来源：软件研究所
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