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Nonlinear clustering-based support vector machine for large data sets

文献类型：期刊论文

作者Wang, Yongqiao2; Zhang, Xun1; Wang, Souyang1; Lai, K. K.3
刊名OPTIMIZATION METHODS & SOFTWARE
出版日期2008
卷号23期号:4页码:533-549
关键词classification kernel clustering support vector machine active learning
ISSN号1055-6788
DOI10.1080/10556780802102453
英文摘要This paper presents a kernel clustering-based support vector machine (KCB-SVM) that generalizes the linear clustering-based support vector machine (CB-SVM) to solve nonlinear classification problems in a novel way. It can not only handles large data sets, but can also have nonlinear discriminant power. By introducing kernel clustering, KCB-SVM unifies the metrics in the clustering and training stages. Elaborately designed clustering features summarize all the information required for further clustering and training, which allows only one scan of the total data sets. Experiments on both artificial and real large data sets show that the KCB-SVM not only achieves better classification accuracy than random sampling, active learning and CB-SVM, but also retains the ability to handle large data sets.
WOS研究方向Computer Science ; Operations Research & Management Science ; Mathematics
语种英语
WOS记录号WOS:000257545100006
出版者TAYLOR & FRANCIS LTD
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/6487]  
专题系统科学研究所
通讯作者Wang, Souyang
作者单位1.Chinese Acad Sci, Inst Syst Sci, Acad Math & Syst Sci, Beijing, Peoples R China
2.Zhejiang Gongshang Univ, Coll Finance, Hangzhou, Zhejiang, Peoples R China
3.City Univ Hong Kong, Dept Management Sci, Kowloon, Hong Kong, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Yongqiao,Zhang, Xun,Wang, Souyang,et al. Nonlinear clustering-based support vector machine for large data sets[J]. OPTIMIZATION METHODS & SOFTWARE,2008,23(4):533-549.
APA Wang, Yongqiao,Zhang, Xun,Wang, Souyang,&Lai, K. K..(2008).Nonlinear clustering-based support vector machine for large data sets.OPTIMIZATION METHODS & SOFTWARE,23(4),533-549.
MLA Wang, Yongqiao,et al."Nonlinear clustering-based support vector machine for large data sets".OPTIMIZATION METHODS & SOFTWARE 23.4(2008):533-549.

入库方式： OAI收割

来源：数学与系统科学研究院

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