Skip to main content
SpringerLink
Log in

A Robust Support Vector Regression Based on Fuzzy Clustering

  • Conference paper
Next-Generation Applied Intelligence (IEA/AIE 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5579))

  • 1559 Accesses

Abstract

Support Vector Regression (SVR) has been very successful in pattern recognition, text categorization and function approximation. In real application systems, data domain often suffers from noise and outliers. When there is noise and/or outliers existing in sampling data, the SVR may try to fit those improper data and obtained systems may have the phenomenon of overfitting. In addition, the memory space for storing the kernel matrix of SVR will be increment with O (N2), where N is the number of training data. In this paper, a robust support vector regression is proposed for nonlinear function approximation problems with noise and outliers.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)

    Book  MATH  Google Scholar 

  2. Gunn, S.R.: Support Vector Machines for Classification and Regression. Technical Report of University of Southampton (1998)

    Google Scholar 

  3. Burges, C.J.C.: A tutorial on Support Vector Machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)

    Article  Google Scholar 

  4. Jen, J.T., Chuang, C.C., Su, S.F.: Support vector interval regression networks for interval regression analysis. Fuzzy Sets and Systems 138, 183–300 (2003)

    MathSciNet  Google Scholar 

  5. Zhou, W., Zhang, L., Jiao, L.: Linear programming support vector machines. Pattern Recognition 35, 2927–2936 (2002)

    Article  MATH  Google Scholar 

  6. Lijuan, C.: Support vector machines experts for time series forecasting. Neurocomputing 51, 321–339 (2003)

    Article  Google Scholar 

  7. Gustavo, C.V., Luis, G.C., Javier, C.M., Jose David, M.G., Emilio, S.O., Luis, A.C., Jose, M.: Robust Support Vector Method for Hyperspectral Data Classification and Knowledge Discovery. IEEE Transactions on Geoscience and Remote Sensing 42(7), 1530–1542 (2004)

    Article  Google Scholar 

  8. Hu, W.J., Song, Q.: An Accelerated Decomposition Algorithm for Robust Support Vector Machines. IEEE Transactions on Circuits and Systems—II: Express Briefs 51(5), 234–240 (2004)

    Article  Google Scholar 

  9. Osuna, E., Freund, R.R., Girosi, F.F.: An improved training algorithm for support vector machines. In: IEEE Workshop Neural Networks for Signal Processing (NNSP 1997), pp. 276–285 (1997)

    Google Scholar 

  10. Lee, Y.J., Huang, S.U.: Reduced Support Vector Machines: A Statistical Theory. IEEE Transactions on Neural Networks 18(1), 1–13 (2007)

    Article  Google Scholar 

  11. Lin, K.M., Lin, C.J.: A Study on Reduced Support Vector Machines. IEEE Transactions on Neural Networks 14(6), 1449–1457 (2003)

    Article  Google Scholar 

  12. Wang, W., Xu, Z.: A heuristic training for support vector regression. Neurocomputing 61, 259–275 (2004)

    Article  Google Scholar 

  13. Chung, C.C., Su, S.F., Hsiao, C.C.: The Annealing Robust Backpropagation(ARBP) Learning Algorithm. IEEE Transactions on Neural Networks 11(5), 1067–1077 (2000)

    Article  Google Scholar 

  14. Jeng, J.T., Chuang, C.C.: Selection of Initial Structures with Support Vector Regression for Fuzzy Neural Networks. International Journal of Fuzzy Systems 6(2), 63–70 (2004)

    MathSciNet  Google Scholar 

  15. Shieh, H.L., Yang, Y.K., Lee, C.N.: A Robust Approach of Fuzzy Clustering on Data Sets with Noise and Outliers. Cybernetics and Systems: An International Journal 37(7), 755–778 (2006)

    Article  MATH  Google Scholar 

  16. Chuang, C.C., Jeng, J.T., Lin, P.T.: Annealing Robust Radial Basis Function Networks for Function Approximation with Outliers. Neurocomputing 56, 123–139 (2004)

    Article  Google Scholar 

  17. Lee, C.C., Chung, P.C., Tsai, J.R., Chang, C.I.: Robust Radial Basis Function Neural Networks. IEEE Transactions Systems, Man, and Cybernetics Part B: Cybernetics 29(6), 674–685 (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Saint John’s University, 499, Sec. 4, TamKing Rd., Taipei, TamSui, 25135, Taiwan

    Horng-Lin Shieh

Authors
  1. Horng-Lin Shieh
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Engineering, National University of Tainan, 700, Tainan, Taiwan

    Been-Chian Chien

  2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  3. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

  4. Department of Computer Science, Texas State University-San Marcos, 601 University Drive, 78666-4616, San Marcos, TX, USA

    Moonis Ali

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Shieh, HL. (2009). A Robust Support Vector Regression Based on Fuzzy Clustering. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02568-6_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02567-9

  • Online ISBN: 978-3-642-02568-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation