Hidden space support vector machines

IEEE Transactions on Neural Networks

Volumn 15, Issue 6, 2004, Pages 1424-1434

  Zhang, Li ^a   Zhou, Weida ^a   Jiao, Licheng ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Artificial neural networks (ANNs); Pattern recognition; Regression estimation; Structural risk; Support vector machines

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; ESTIMATION; LEARNING SYSTEMS; PATTERN RECOGNITION; REGRESSION ANALYSIS;

HIDDEN SPACE SUPPORT VECTOR MACHINES; REGRESSION ESTIMATION; STRUCTURAL RISK; SUPPORT VECTOR MACHINES;

NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER ANALYSIS; COMPUTER SIMULATION; DECISION SUPPORT SYSTEM; EVALUATION; FEEDBACK SYSTEM; METHODOLOGY; NONLINEAR SYSTEM; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; COMPUTING METHODOLOGIES; DECISION SUPPORT TECHNIQUES; FEEDBACK; LOGISTIC MODELS; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; PATTERN RECOGNITION, AUTOMATED;

EID: 9244235011     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2004.831161     Document Type: Article

References (31)

1. V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
2. V. Vapnik, "An overview of statistical learning theory," IEEE Trans. Neural Networks, vol. 10, pp. 988-999, 1999.
3. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines. Cambridge, UK: Cambridge University Press, 2000.
4. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, vol. 2, pp. 121-167, 1998.
5. A. Smola, "Regression estimation with support vector learning machines," Master's thesis, Technische Universtät München, Germany, 1996.
6. J. Mercer, "Function of positive and negative type and their connection with the theory of integral equations," Philos. Trans. Roy. Soc. London, vol. A 209, pp. 415-446, 1909.
7. S. Saitoh, Theory of Reproducing Kernels and Its Applications. Harlow, UK: Longman Scientific & Technical, 1988.
8. N. Aronszajn, "Theory of reproducing kernels," Trans. Amer. Math. Soc., vol. 686, pp. 337-404, 1950.
9. S. Haykin, Neural Networks, a Comprehensive Foundation, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 1999.
10. K. Gurney, Introduction to Neural Networks. London, UK: UCL, 1997.
11. L. C. Jiao, Neural Networks System Theory (in Chinese). Xi'an, China: Xidian University Press, 1996.
12. Neural Networks Theory Technology and Application, IEEE Technology Updates Series, P. K. Simpson, Ed., The Inst. Elect. Electron. Eng.., New York, 1996.
13. F. Luo, Applied Neural Networks for Signal Processing. Cambridge, UK: Cambridge, 1997.
14. D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing. Cambridge, MA: MIT Press, 1986, vol. 1-2.
15. M. J. D. Powell, "Radial basis functions for multivariable interpolation: a review," in Algorithms for Approximation, J. C. Mason and M. G. Cox, Eds. Oxford: Clarendon, 1987.
16. K. Hornik, M. Stinchcombe, and H. White, "Multi-layer feedforward networks are universal approximators," Neural Networks, vol. 2, pp. 359-366, 1989.
17. H. N. Mhaskar and C. A. Micchelli, "Approximation by superposition of a sigmoidal function and radial basis functions," Advances in Applied Mathematics, vol. 13, pp. 350-373, 1992.
18. J. A. K. Suykens and J. Vandewalle, "Training multiplayer perceptron classifiers based on a modified support vector method," IEEE Trans. Neural Networks, vol. 10, July 1999.
19. J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle, Least Squares Support Vector Machines, Singapore: World Scientific, 2002.
20. C. Saunders, M. O. Stitson, J. Weston, L. Bottou, B. Schölkopf, and A. Smola, "Support Vector Machine - Reference Manual," Dept. Computer Science, Royal Holloway, Univ. London, Egham, UK, Technical Report CSD-TR-98-03, 1998.
21. G. Wahba, "Support vector machines, reproducing kernel hilbert spaces and the randomized GACV," in Advances in Kernel Methods - Support Vector Learning, B. Schölkopf, C. J. C. Burges, and A. J. Smola, Eds. Cambridge, MA: MIT Press, 1999, pp. 69-88.
22. T. M. Cover, "Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition," IEEE Trans. Electron. Comp., vol. EC-14, pp, 326-334, 1965.
23. L. K. Jones, "A simple lemma on greedy approximation in Hilbert space and convergence rates for projection pursuit regression and neural network training," The Annals of Statistics, vol. 20, pp. 608-613, Mar. 1992.
24. G. Cybenko, "Approximation by superposition of a sigmoidal function," Math. Contr., Signal and Syst., vol. 2, pp. 303-314, 1989.
25. A. R. Barron, "Universal approximation bounds for superposition of a sigmoidal function," IEEE Trans. Inform. Theory, vol. 39, pp. 930-945, 1993.
26. J. Park and I. W. Sandberg, "Universal approximation using radial basis function networks," Neural Comput., vol. 3, pp. 246-257, 1991.
27. M. J. D. Powell, "The theory of radial basis function approximation," in Advances in Numerical Analysis III, Wavelets, Subdivision Algorithms and Radial Basis Functions, W. A. Light, Ed. Oxford, UK: Clarendon, 1992, pp. 105-210.
28. H. N. Mhaskar, "Neural networks for optimal approximation of smooth and analytic functions," Neural Comput., vol. 8, pp. 164-177, 1996.
29. K. J. Lang and M. J. Witbrock, "Learning to tell two spirals apart," in Proc. 1989 Connectionis Models Summer School, 1989, pp. 52-61.
30. B. Scholkopf, C. Burges, and V. Vapnik, "Extracting support data for a given task," in The Proc. First Int. Conf. Knowledge Discovery & Data Mining, M. Fayyad and R. Uthurusamy, Eds. Menlo Park, CA: AAAI, 1995.
31. UCI Repository of Machine Learning Databases, C. J. Merz and P. M. Murphy. (1996). http://www.ics.uci.edu/~mlearn/MLPepository.html [Online]