Differentiation of syndromes with SVM

CASIA OpenIR > 09年以前成果

	Differentiation of syndromes with SVM
	Sun, Zhanquan; Xi, Guangcheng; Yi, Jianqiang; Wang, J; Yi, Z; Zurada, JM; Lu, BL; Yin, H
发表期刊	ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS
	2006
卷号	3973 页码:786-791
文章类型	Article
摘要	Differentiation of syndromes is the kernel theory of Traditional Chinese Medicine (TCM). How to diagnose syndromes correctly with scientific means according to symptoms is the first problem in TCM. Several modem approaches have been applied, but no satisfied results have been obtained because of the complexity of diagnosis procedure. Support Vector Machine (SVM) is a new classification technique and has drawn much attention on this topic in recent years. In this paper, we combine non-linear Principle Component Analysis (PCA) neural network with multi-class SVM to realize differentiation of syndromes. Non-linear PCA is used to preprocess clinical data to save computational cost and reduce noise. The multi-class SVM takes the non-linear principle components as its inputs and determines a corresponding syndrome. Analyzing of a TCM example shows its effectiveness.
WOS标题词	Science & Technology ; Technology
关键词[WOS]	MULTIOBJECTIVE OPTIMIZATION ; RELIABILITY
收录类别	ISTP ; SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号	WOS:000239485300115
引用统计	被引频次：3[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/173211/9327
专题	09年以前成果
作者单位	Chinese Acad Sci, Inst Automat, Key Lab Complex Syst & Intelligence Sci, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Sun, Zhanquan,Xi, Guangcheng,Yi, Jianqiang,et al. Differentiation of syndromes with SVM[J]. ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS,2006,3973:786-791.
APA	Sun, Zhanquan.,Xi, Guangcheng.,Yi, Jianqiang.,Wang, J.,Yi, Z.,...&Yin, H.(2006).Differentiation of syndromes with SVM.ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS,3973,786-791.
MLA	Sun, Zhanquan,et al."Differentiation of syndromes with SVM".ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS 3973(2006):786-791.

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