A Radial Basis Function Approach to Pattern Recognition and Its Applications

ETRI Journal

关键词: Pattern Recognition;    Radial Basis Functions;    RC Algorithm;    Predictive Modeling;    Heart Disease Diagnosis;   

Others  :  1184325
DOI  :  10.4218/etrij.00.0100.0201

【 摘 要 】

Pattern recognition is one of the most common problems encountered engineering and scientific disciplines, which involves developing prediction or classification models from historic data or training samples. This paper introduces a new approach, called t

   

【 参考文献 】

• [1]C.M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995.
• [2]B.D. Ripley, Pattern Recognition and Neural Networks. Cambridge University Press, 1996.
• [3]F. Girosi and T. Poggio, "Networks and the best approximation property," Biological Cybernetics, vol. 63, no. 3, pp. 169-176.
• [4]M.J.D. Powell, "The theory of radial basis function approximation in 1990," Advances in Numerical Analysis, Wavelets, Subdivision Algorithms and Radial Basis Functions, edited by W. A. Light, Oxford University Press, vol. 2, 1992, pp. 105-210.
• [5]J.H. Friedman, "On bias, variance, 0/1-loss, and the curse-of-dimensionality," Data Mining and Knowledge Discovery, vol. 1, no. 1, pp. 55-77.
• [6]S. Geman, E. Bienenstock and R. Doursat, "Neural networks and the bias/variance dilemma," Neural Computation, vol. 4, 1992, pp. 1-58.
• [7]J. Moody and C.J. Darken, "Fast learning in networks of locally-tuned processing units," Neural Computation, vol. 1, 1989, pp. 281-294.
• [8]S. Chen, C.F.N. Cowan and P.M. Grant, "Orthogonal least squares learning algorithm for radial basis function networks," IEEE Transactions on Neural Networks, vol. 2, no. 2, pp. 302-309.
• [9]M. Shin, Design and Evaluation of Radial Basis Function Model for Function Approximation.Ph.D Dissertation, Syracuse University, 1998.
• [10]Dimitris Bertsimas, David Gamarnik and John N. Tsitsiklis, "Estimation of time-varying parameters in statistical models: An optimization approach," Proceedings of the Annual ACM Conference on Computational Learning Theory, pp. 314-324.
• [11]G.H. Golub and C.F. Van Loan, Matrix Computations. The Johns Hopkins University Press, The Third Edition, 1996.
• [12]L. Prechelt, "PROBEN1-A Set of Neural Network Benchmark Problems and Benchmarking Rules," Technical Reports 21/94, Universitat Karlsruhe, Germany, September 1994.
• [13]M. Shin and A.L. Goel, "An RBF classifier based framework for software quality evaluation," Proceedings of the International Conference on Computational Intelligence for Modeling, Control and Automation, Vienna, Austria, February 1999.
• [14]M. Shin and A.L. Goel, "Knowledge discovery and validation in software metrics databases," Proceedings of Data Mining and Knowledge Discovery: Theory, Tools and Technology, Orlando, Florida, April 1999.
• [15]V.N. Vapnik, Statistical Learning Theory, John Wiley, 1998.