Differentiation of Syndromes with SVM

  • Zhanquan Sun
  • Guangcheng Xi
  • Jianqiang Yi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


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 modern 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.


Support Vector Machine Traditional Chinese Medicine Multiobjective Optimization Principle Component Analysis Support Vector Machine Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhanquan Sun
    • 1
  • Guangcheng Xi
    • 1
  • Jianqiang Yi
    • 1
  1. 1.Key Laboratory of Complex Systems and Intelligence Science, Institute of AutomationAcademy of SciencesBeijingChina

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