A Competitive Approach to Neural Device Modeling: Support Vector Machines

  • Nurhan Türker
  • Filiz Güneş
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


Support Vector Machines (SVM) are a system for efficiently training linear learning machines in the kernel induced feature spaces, while respecting the insights provided by the generalization theory and exploiting the optimization theory. In this work, Support Vector Machines are employed for the nonlinear regression. The nonlinear regression ability of the Support Vector Machines has been demonstrated by forming the SVM model of a microwave transistor and it has been compared with its neural model.


Support Vector Machine Support Vector Machine Model Neural Model Support Vector Regression Machine Empirical Risk Minimization 


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  1. 1.
    Poggio, T., Torre, V., Koch, C.: Computational vision and regularization theory. Nature 317, 314–319 (1985)CrossRefGoogle Scholar
  2. 2.
    Zhang, Q.J., Gupta, K.C.: Neural Networks For RF and Microwave Design. Artech House Publishers (2000)Google Scholar
  3. 3.
    Gunes, F., Gurgen, F., Torpi, H.: Signal-Noise Neural Network Model For Active Microwave Devices. IEE P-Circ Dev. Syst. 143 (1996)Google Scholar
  4. 4.
    Gunes, F., Turker, N.: Artificial Neural Networks In Their Simplest Forms For Analysis And Synthesis Of RF/Microwave Planar Transmission Lines. Int. J. RF Microw C E 15, 587–600 (2005)CrossRefGoogle Scholar
  5. 5.
    Kuhn, H., Tucker, A.: Nonlinear programming. In: The proceedings of 2nd Berkeley Symposium on Mathematical Statistics and Probabilistics, pp. 481–492. University of California Press, Berkeley (1951)Google Scholar
  6. 6.
    Mercer, J.: Functions of positive and negative type and their connection with the theory of integral equations. Philos. Trans. Roy. Soc. London, 415–446 (1909)Google Scholar
  7. 7.
    Cristianini, N., Shawe-Taylor, J.: An Introduction To Support Vector Machines And Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)Google Scholar
  8. 8.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, N.Y (1995) ISBN 0-387- 94559-8MATHGoogle Scholar
  9. 9.
    Gunn, S.R., Brown, M., Bossley, K.M.: Network performance assessment for neurofuzzy data modelling. Intelligent Data Analysis. In: Ben-David, S. (ed.) EuroCOLT 1997. LNCS, vol. 1208, pp. 313–323. Springer, Heidelberg (1997)Google Scholar
  10. 10.
    Gunn, S.R.: Support Vector Machines for Classification and Regression. Technical Report, University of Southampton (1998)Google Scholar
  11. 11.
    Vapnik, V., Golowich, S., Smola, A.: Support vector method for function approximation, regression estimation, and signal processing. In: Advances in Neural Information Processing Systems, vol. 9, pp. 281–287. MIT Press, Cambridge (1997)Google Scholar
  12. 12.
    Chang, C., Lin, C.: LIBSVM: a library for support vector machines. Software (2001), Available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nurhan Türker
    • 1
  • Filiz Güneş
    • 1
  1. 1.Electrical and Electronics Faculty, Department of Electronics and Communication EngineeringYıldız Technical UniversityYıldız, IstanbulTurkey

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