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.
Similar content being viewed by others
References
Vapnik V N. Statistical Learning Theory. New York: A Wiley-Interscience Publication, 1998
Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995
Vapnik V N. An overview of statistical learning theory. IEEE Transactions on Neural Networks, 1999, 10(5): 988–999
Qi B Z, Zhang X G. Pattern Recognition (in Chinese). Beijing: Tsinghua University Press, 1999
Zhang X G. Introduction to statistical learning theory and support vector machines. Acta Automat Sin (in Chinese), 2000, 26(1): 32–44
Liu H C, Ma S Y. The research status of support vector machine. J Image Graph (in Chinese), 2002, 7(6): 618–623
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
Raudys S. How good are support vector machines? Neural Networks, 2000, 13(1): 17–19
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
Tsai C F. Training support vector machines based on stacked generalization for image classification. Neurocomputing, 2005, 64: 497–503
Kikuchi T, Abe S. Comparison between error correcting output codes and fuzzy support vector machines. Pattern Recognition Letters, 2005, 26(12): 1937–1945
Cawley G C, Talbot N L C. Improved sparse least-squares support vector machines. Neurocomputing, 2002, 48(1–4): 1025–1031
Zhang Y Q, Shen D G. Design efficient support vector machine for fast classification. Pattern Recognition, 2005, 38(1): 157–161
Sugeno M. Theory of fuzzy integrals and its applications. Doctoral Thesis, Tokyo Institute of Technology, 1974
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
Wang Z Y, George J K. Fuzzy Measure Theory. New York: Plenum Press, 1992
Ha M H, Wu C X. Fuzzy Measure and Fuzzy Integral (in Chinese). Beijing: Science Press, 1998
Weber S. Two integrals and some modified versions critical remarks. Fuzzy Sets and Systems, 1986, 20: 97–105
Author information
Authors and Affiliations
Corresponding author
Rights 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
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/s11432-006-0372-8