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Differentiation of Syndromes with SVM

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

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.

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References

  1. Huang, H.Z., Wu, W.D., Liu, C.S.: A Coordination Method for Fuzzy Multi-objective Optimization of System Reliability. Journal of Intelligent and Fuzzy Systems 16, 213–220 (2005)

    MATH  Google Scholar 

  2. Huang, H.Z., Li, Y.H., Xue, L.H.: A Comprehensive Evaluation Model for Assessments of Grinding Machining Quality. Key Engineering Materials 291-292, 157–162 (2005)

    Article  Google Scholar 

  3. Cortes, C., Vapnik, V.: Support Vector Networks. Machine Learning 20, 273–297 (1995)

    MATH  Google Scholar 

  4. Burges, J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)

    Article  Google Scholar 

  5. Xu, L.: Least Mean Square Error Reconstruction Principle for Self-organization. Neural Networks 6, 627–648 (1993)

    Article  Google Scholar 

  6. Karhunen, J., Joutsensalo, J.: Representation and Separation of Signals Using Nonlinear PCA type learning. Neural Networks 7, 113–127 (1994)

    Article  Google Scholar 

  7. Huang, H.Z., Tian, Z.G., Zuo, M.J.: Intelligent Interactive Multiobjective Optimization Method and Its application to Reliability Optimization. IIE Transactions 37, 983–993 (2005)

    Article  Google Scholar 

  8. Bian, Q., He, Y.M., Shi, X.C., Wang, H.W.: Neural Network Model of TCM Syndromes Based on MFBP Learning Algorithm. Chinese Journal of Basic Medicine in Traditional Chinese Medicine 7, 66–69 (2001)

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Sun, Z., Xi, G., Yi, J. (2006). Differentiation of Syndromes with SVM. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_115

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  • DOI: https://doi.org/10.1007/11760191_115

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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