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学科主题: Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications
题名:
theoretically optimal parameter choices for support vector regression machines with noisy input
作者: Wang ST ; Zhu JG ; Chung FL ; Lin Q ; Hu DW
关键词: 复合事件,检测regularized linear regression ; support vectors ; Huber loss functions ; norm-r loss functions
刊名: SOFT COMPUTING
发表日期: 2005
卷: 9, 期:10, 页:732-741
收录类别: sci ; acm ; cnki
部门归属: So Yangtze Univ, Sch Informat Engn, Wuxi, Peoples R China. Nanjing Univ Sci & Tech, Dept Comp Sci & Engn, Nanjing, Peoples R China. HongKong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China. Natl Def Univ Sci & Tech, Sch Automat, Changsha, Peoples R China. Chinese Acad Sci, Inst Software, Comp Sci Lab, Beijing, Peoples R China.
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。
语种: 英语
内容类型: 期刊论文
URI标识: http://ir.iscas.ac.cn/handle/311060/12442
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Wang ST,Zhu JG,Chung FL,et al. theoretically optimal parameter choices for support vector regression machines with noisy input[J]. SOFT COMPUTING,2005-01-01,9(10):732-741.
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