Skip to main content
Log in

The key theorem and the bounds on the rate of uniform convergence of learning theory on Sugeno measure space

  • Published:
Science in China Series F Aims and scope Submit manuscript

Abstract

Some properties of Sugeno measure are further discussed, which is a kind of typical nonadditive measure. The definitions and properties of g λ random variable and its distribution function, expected value, and variance are then presented. Markov inequality, Chebyshev’s inequality and the Khinchine’s Law of Large Numbers on Sugeno measure space are also proven. Furthermore, the concepts of empirical risk functional, expected risk functional and the strict consistency of ERM principle on Sugeno measure space are proposed. According to these properties and concepts, the key theorem of learning theory, the bounds on the rate of convergence of learning process and the relations between these bounds and capacity of the set of functions on Sugeno measure space are given.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Vapnik V N. Statistical Learning Theory. New York: A Wiley-Interscience Publication, 1998

    Google Scholar 

  2. Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995

    Google Scholar 

  3. Vapnik V N. An overview of statistical learning theory. IEEE Transactions on Neural Networks, 1999, 10(5): 988–999

    Article  Google Scholar 

  4. Qi B Z, Zhang X G. Pattern Recognition (in Chinese). Beijing: Tsinghua University Press, 1999

    Google Scholar 

  5. Zhang X G. Introduction to statistical learning theory and support vector machines. Acta Automat Sin (in Chinese), 2000, 26(1): 32–44

    Google Scholar 

  6. Liu H C, Ma S Y. The research status of support vector machine. J Image Graph (in Chinese), 2002, 7(6): 618–623

    Google Scholar 

  7. Zheng H J, Zhou X, Bi D Y. The summary of statistical learning theory and support vector machines. Modern Electron Techn (in Chinese), 2003, 4: 59–61

    Google Scholar 

  8. Raudys S. How good are support vector machines? Neural Networks, 2000, 13(1): 17–19

    Article  Google Scholar 

  9. Francis E H, Tay F E H, Cao L J. Application of support vector machines in financial time series forecasting. Omega, 2001, 29: 309–317

    Article  Google Scholar 

  10. Tsai C F. Training support vector machines based on stacked generalization for image classification. Neurocomputing, 2005, 64: 497–503

    Article  Google Scholar 

  11. Kikuchi T, Abe S. Comparison between error correcting output codes and fuzzy support vector machines. Pattern Recognition Letters, 2005, 26(12): 1937–1945

    Article  Google Scholar 

  12. Cawley G C, Talbot N L C. Improved sparse least-squares support vector machines. Neurocomputing, 2002, 48(1–4): 1025–1031

    Article  Google Scholar 

  13. Zhang Y Q, Shen D G. Design efficient support vector machine for fast classification. Pattern Recognition, 2005, 38(1): 157–161

    Article  Google Scholar 

  14. Sugeno M. Theory of fuzzy integrals and its applications. Doctoral Thesis, Tokyo Institute of Technology, 1974

  15. Ha M H, Wang R S, Zhang L. Fuzzy integral method applied in material flow engineering. Fuzzy Systems Mathem (in Chinese), 2004, 18(4): 72–76

    Google Scholar 

  16. Wang Z Y, George J K. Fuzzy Measure Theory. New York: Plenum Press, 1992

    Google Scholar 

  17. Ha M H, Wu C X. Fuzzy Measure and Fuzzy Integral (in Chinese). Beijing: Science Press, 1998

    Google Scholar 

  18. Weber S. Two integrals and some modified versions critical remarks. Fuzzy Sets and Systems, 1986, 20: 97–105

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ha Minghu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ha, M., Li, Y., Li, J. et al. The key theorem and the bounds on the rate of uniform convergence of learning theory on Sugeno measure space. SCI CHINA SER F 49, 372–385 (2006). https://doi.org/10.1007/s11432-006-0372-8

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-006-0372-8

Keywords

Navigation