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A Smoothing Multiple Support Vector Machine Model

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Part of the book series:Lecture Notes in Computer Science ((LNTCS,volume 3971))

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Abstract

In this paper, we study a smoothing multiple support vector machine (SVM) by using exact penalty function. First, we formulate the optimization problem of multiple SVM as an unconstrained and nonsmooth optimization problem via exact penalty function. Then, we propose a two-order differentiable function to approximately smooth the exact penalty function, and get an unconstrained and smooth optimization problem. By error analysis, we can get approximate solution of multiple SVM by solving its approximately smooth penalty optimization problem without constraint. Finally, we give a corporate culture model by using multiple SVM as a factual example. Compared with artificial neural network, the precision of our smoothing multiple SVM which is illustrated with the numerical experiment is better.

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References

  1. Ko, J., Byun, H.: Multi-class Support Vector Machines with Case-Based Combination for Face Recognition. In: Petkov, N., Westenberg, M.A. (eds.) CAIP 2003. LNCS, vol. 2756, pp. 623–629. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  2. Denison, D.R.: Corporate Culture and Organizational Effectiveness. John Wiley & Sons, Inc., New York (1990)

    Google Scholar 

  3. Chin, K.-S., Pun, K.F., Ho, A.S.K., Lau, H.: A Measurement-Communication-Recognition Framework of Corporate Culture Change: An Empirical Study. Human Factors and Ergonomics in Manufacturing 12, 365–382 (2002)

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Lee, K.L., Billings, S.A.: Time Series Prediction Using Support Vector Machines, the Orthogonal and the Regularized Orthogonal Least-Squares Algorithms. International Journal of Systems Science 33, 811–821 (2002)

    Article MATH MathSciNet  Google Scholar 

  6. Lee, Y.-J., Mangasarin, O.L.: SSVM: A Smooth Support Vector Machine for Classification. Computational Optimization and Applications 20, 5–22 (2001)

    Article MATH MathSciNet  Google Scholar 

  7. Quan, Y., Yang, J.: An Improved Parameter Tuning Method for Support Vector Machines. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 607–610. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  8. Zenios, S.A., Pinar, M.C., Dembo, R.S.: A Smooth Penalty Function Algorithm for Network-Structured Problems. European Journal of Operation Research 64, 258–277 (1993)

    Article  Google Scholar 

  9. Meng, Z.Q., Dang, C.Y., Zhou, G., Zhu, Y., Jiang, M.: A New Neural Network for Nonlinear Constrained Optimization Problems. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3173, pp. 406–411. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  10. Yang, X.Q., Meng, Z.Q., Huang, X.X., Pong, G.T.Y.: Smoothing Nonlinear Penalty Functions for Constrained Optimization. Numerical Functional Analysis Optimization 24, 351–364 (2003)

    Article MATH MathSciNet  Google Scholar 

  11. Rubinov, A.M., Glover, B.M., Yang, X.Q.: Decreasing Functions with Applications to Penalization. SIAM Journal Optimization 10, 289–313 (1999)

    Article MATH MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. College of Business and Administration, Nanjing University of Aeronautics and Astronautics, Jiangshu, China

    Huihong Jin & Xuanxi Ning

  2. College of Business and Administration, Zhejiang University of Technology, Zhejiang, 310032, China

    Huihong Jin & Zhiqing Meng

Authors
  1. Huihong Jin

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  2. Zhiqing Meng

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  3. Xuanxi Ning

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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Jin, H., Meng, Z., Ning, X. (2006). A Smoothing Multiple Support Vector Machine Model. 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 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_138

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