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Classification based on dimension transposition for high dimension data

文献类型：期刊论文

作者He, Qing; Zhao, Xiurong; Shi, Zhongzhi
刊名SOFT COMPUTING
出版日期2007-02-01
卷号11期号:4页码:329-334
关键词dimension transposition SVM HSC Jordan Curve Theorem
ISSN号1432-7643
DOI10.1007/s00500-006-0085-3
英文摘要Based on Jordan Curve Theorem, a universal classification method called hyper surface classification (HSC) has recently been proposed. Experimental results are exciting, which show that in three-dimensional space, this method works fairly well in both accuracy and efficiency even for large size data up to 10(7). However, designing a number of new classifiers is needed with the growing of feature dimension. To solve the problem, a kind of efficient dimension transposition method that is suitable for HSC and without losing any essential information is put forward in this paper. The dimension transposition method rearrange all of the numerals in the higher dimensional data to lower dimensional data without changing each numeral, but only change their position according to some orders. The experiment shows that the method can classify high dimension data with high accuracy.
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000241949900005
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/11034]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者He, Qing
作者单位Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100080, Peoples R China
推荐引用方式
GB/T 7714		 He, Qing,Zhao, Xiurong,Shi, Zhongzhi. Classification based on dimension transposition for high dimension data[J]. SOFT COMPUTING,2007,11(4):329-334.
APA He, Qing,Zhao, Xiurong,&Shi, Zhongzhi.(2007).Classification based on dimension transposition for high dimension data.SOFT COMPUTING,11(4),329-334.
MLA He, Qing,et al."Classification based on dimension transposition for high dimension data".SOFT COMPUTING 11.4(2007):329-334.

入库方式： OAI收割

来源：计算技术研究所

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