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Kernel projection algorithm for large-scale SVM problems
Wang, JQ; Tao, Q; Wang, J
2002-09-01
发表期刊JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
卷号17期号:5页码:556-564
文章类型Article
摘要Support Vector Machine (SVM) has become a very effective method in statistical machine learning and it has proved that training SVM is to solve Nearest Point pair Problem (NPP) between two disjoint closed convex sets. Later Keerthi pointed out that it is difficult to apply classical excellent geometric algorithms directly to SVM and so designed a new geometric algorithm for SVM. In this article, a new algorithm for geometrically solving SVM, Kernel Projection Algorithm, is presented based on the theorem on fixed-points of projection mapping. This new algorithm makes it easy to apply classical geometric algorithms to solving SVM and is more understandable than Keerthi's. Experiments show that the new algorithm can also handle large-scale SVM problems. Geometric algorithms for SVM, such as Keerthi's algorithm, require that two closed convex sets be disjoint and. otherwise the algorithms are meaningless. In this article, this requirement will be guaranteed in theory by using the theoretic result on universal kernel functions.
关键词Svm Npp Mnp Feature Mapping Projection Fixed-point Universal Kernel
WOS标题词Science & Technology ; Technology
收录类别SCI
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Hardware & Architecture ; Computer Science, Software Engineering
WOS记录号WOS:000178233400005
引用统计
被引频次:5[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/9853
专题09年以前成果
作者单位Chinese Acad Sci, Inst Automat, Beijing 100080, Peoples R China
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GB/T 7714
Wang, JQ,Tao, Q,Wang, J. Kernel projection algorithm for large-scale SVM problems[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2002,17(5):556-564.
APA Wang, JQ,Tao, Q,&Wang, J.(2002).Kernel projection algorithm for large-scale SVM problems.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,17(5),556-564.
MLA Wang, JQ,et al."Kernel projection algorithm for large-scale SVM problems".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 17.5(2002):556-564.
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