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	A Fast Two-Stage Classification Method of Support Vector Machines
	Chen, Jin ; Wang, Cheng ; Wang C(王程) ; Wang, Runsheng
	2008
关键词	FEATURE SUBSET-SELECTION REMOTE-SENSING IMAGES SVM
英文摘要	Classification of high-dimensional data generally requires enormous processing time. In this paper, we present a fast two-stage method of support vector machines, which includes a feature reduction algorithm and a fast multiclass method. First, principal component analysis is applied to the data for feature reduction and decorrelation, and then a feature selection method is used to further reduce feature dimensionality. The criterion based on Bhattacharyya distance is revised to get rid of influence of some binary problems with large distance. Moreover, a simple method is proposed to reduce the processing time of multiclass problems, where one binary SVM with the fewest support vectors (SVs) will be selected iteratively to exclude the less similar class until the final result is obtained. Experimented with the hyperspectral data 92AV3C, the results demonstrate that the proposed method can achieve a much faster classification and preserve the high classification accuracy of SVMs.
语种	英语
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/70980]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Chen, Jin,Wang, Cheng,Wang C,et al. A Fast Two-Stage Classification Method of Support Vector Machines[J],2008.
APA	Chen, Jin,Wang, Cheng,王程,&Wang, Runsheng.(2008).A Fast Two-Stage Classification Method of Support Vector Machines..
MLA	Chen, Jin,et al."A Fast Two-Stage Classification Method of Support Vector Machines".(2008).

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