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Transformation of Input Domain for SVM in Regression Task

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Man-Machine Interactions 3

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 242))

Abstract

Support vector machines (SVM) and neuro-fuzzy systems (NFS) are efficient tools for regression tasks. The problem of the SVMs is the proper choice of kernel functions. Our idea is to transform the task’s domain with NFS so that linear kernel can be applied. The paper is accompanied by numerical experiments.

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References

  1. Chen, Y.: Support vector machines and fuzzy systems. In: Maimon, O., Rokach, L. (eds.) Soft Computing for Knowledge Discovery and Data Mining, pp. 205–223. Springer (2008)

    Google Scholar 

  2. Chiang, J.H., Hao, P.Y.: Support vector learning mechanism for fuzzy rule-based modeling: a new approach. IEEE Transactions on Fuzzy Systems 12(1), 1–12 (2004)

    Article  Google Scholar 

  3. Czogała, E., Łęski, J.: Fuzzy and Neuro-Fuzzy Intelligent Systems. STUDFUZZ, vol. 47. Springer, Heidelberg (2000)

    Book  MATH  Google Scholar 

  4. Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact, well separated clusters. Journal of Cybernetics 3(3), 32–57 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  5. Frank, A., Asuncion, A.: UCI machine learning repository (2010)

    Google Scholar 

  6. Joachims, T.: Making large-scale SVM learning practical. In: Schölkopf, B., Burges, C.J.C., Smola, A. (eds.) Advances in Kernel Methods – Support Vector Learning, pp. 169–184. MIT Press, Cambridge (1999)

    Google Scholar 

  7. Łęski, J., Czogała, E.: A new artificial neural network based fuzzy inference system with moving consequents in if-then rules and selected applications. Fuzzy Sets and Systems 108(3), 289–297 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  8. Makridakis, S.G., Wheelwright, S.C., Hyndman, R.J.: Forecasting: Methods and Applications, 3rd edn. Wiley, New York (1998)

    Google Scholar 

  9. Sikora, M., Krzykawski, D.: Application of data exploration methods in analysis of carbon dioxide emission in hard-coal mines dewater pump stations. Mechanization and Automation of Mining 413(6) (2005)

    Google Scholar 

  10. Sikora, M., Sikora, B.: Application of machine learning for prediction a methane concentration in a coal-mine. Archives of Mining Sciences 51(4), 475–492 (2006)

    Google Scholar 

  11. Simiński, K.: Neuro-fuzzy system with hierarchical domain partition. In: Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 2008), pp. 392–397. IEEE Computer Society Publishing (2008)

    Google Scholar 

  12. Simiński, K.: Two ways of domain partition in fuzzy inference system with parametrized consequences: Clustering and hierarchical split. In: 10th International Ph. D. Workshop (OWD 2008), pp. 103–108 (2008)

    Google Scholar 

  13. Simiński, K.: Patchwork neuro-fuzzy system with hierarchical domain partition. In: Kurzynski, M., Wozniak, M. (eds.) Computer Recognition Systems 3. AISC, vol. 57, pp. 11–18. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  14. Simiński, K.: Remark on membership functions in neuro-fuzzy systems. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds.) Man-Machine Interactions. AISC, vol. 59, pp. 291–297. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  15. Simiński, K.: Rule weights in neuro-fuzzy system with hierarchical domain partition. International Journal of Applied Mathematics and Computer Science 20(2), 337–347 (2010)

    MATH  Google Scholar 

  16. Simiński, K.: Analysis of new method of initialisation of neuro-fuzzy systems with support vector machines. Theoretical and Applied Informatics 24(3), 243–254 (2012)

    Google Scholar 

  17. Späth, H.: Mathematical algorithms for linear regression. Academic Press Professional, Inc., San Diego (1992)

    MATH  Google Scholar 

  18. Vapnik, V.: Statistical Learning Theory. Wiley-Interscience, New York (1998)

    MATH  Google Scholar 

  19. Yeh, I.C.: Modeling of strength of high-performance concrete using artificial neural networks. Cement and Concrete Research 28(12), 1797–1808 (1998)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Krzysztof Simiński

Authors
  1. Krzysztof Simiński
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Correspondence to Krzysztof Simiński .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dr. Aleksandra Gruca

  2. Polish Academy of Sciences and Silesian University of Technology, Gliwice, Poland

    Tadeusz Czachórski

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

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Simiński, K. (2014). Transformation of Input Domain for SVM in Regression Task. In: Gruca, D., Czachórski, T., Kozielski, S. (eds) Man-Machine Interactions 3. Advances in Intelligent Systems and Computing, vol 242. Springer, Cham. https://doi.org/10.1007/978-3-319-02309-0_46

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  • DOI: https://doi.org/10.1007/978-3-319-02309-0_46

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02308-3

  • Online ISBN: 978-3-319-02309-0

  • eBook Packages: EngineeringEngineering (R0)

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