Building Sparse Multiple-Kernel SVM Classifiers

doi:10.1109/TNN.2009.2014229

	Building Sparse Multiple-Kernel SVM Classifiers
	Hu, Mingqing 1; Chen, Yidiang 1; Kwok, James Tin-Yau 2
	2009-05-01
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS
ISSN	1045-9227
卷号	20 期号:5 页码:827-839
摘要	The support vector machines (SVMs) have been very successful in many machine learning problems. However, they can be slow during testing because of the possibly large number of support vectors obtained. Recently, Wu et al. (2005) proposed a sparse formulation that restricts the SVM to use a small number of expansion vectors. In this paper, we further extend this idea by integrating with techniques from multiple-kernel learning (MKL). The kernel function in this sparse SVM formulation no longer needs to be fixed but can be automatically learned as a linear combination of kernels. Two formulations of such sparse multiple-kernel classifiers are proposed. The first one is based on a convex combination of the given base kernels, while the second one uses a convex combination of the so-called "equivalent" kernels. Empirically, the second formulation is particularly competitive. Experiments on a large number of toy and real-world data sets show that the resultant classifier is compact and accurate, and can also be easily trained by simply alternating linear program and standard SVM solver.
关键词	Gradient projection kernel methods multiple-kernel learning (MKL) sparsity support vector machine (SVM)
DOI	10.1109/TNN.2009.2014229
收录类别	SCI
语种	英语
资助项目	National High Technology Research and Development Program of China[2007AA01Z305] ; National Natural Science Foundation of China[60775027] ; Research Grants Council of the Hong Kong Special Administrative Region, China[614907]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000265748600007
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：84[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/11795
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Hu, Mingqing
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100080, Peoples R China 2.Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Hu, Mingqing,Chen, Yidiang,Kwok, James Tin-Yau. Building Sparse Multiple-Kernel SVM Classifiers[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS,2009,20(5):827-839.
APA	Hu, Mingqing,Chen, Yidiang,&Kwok, James Tin-Yau.(2009).Building Sparse Multiple-Kernel SVM Classifiers.IEEE TRANSACTIONS ON NEURAL NETWORKS,20(5),827-839.
MLA	Hu, Mingqing,et al."Building Sparse Multiple-Kernel SVM Classifiers".IEEE TRANSACTIONS ON NEURAL NETWORKS 20.5(2009):827-839.