A novel radial basis function neural network for discriminant analysis

IEEE Transactions on Neural Networks

Volumn 17, Issue 3, 2006, Pages 604-612

  Yang, Zheng Rong ^a  

^a UNIVERSITY OF EXETER   (United Kingdom)

Author keywords

Bayesian method; Discriminant analysis; Radial basis function neural networks (RBFNNs)

Indexed keywords

COMPUTER SIMULATION; LEARNING ALGORITHMS; MAXIMUM PRINCIPLE; PARAMETER ESTIMATION;

BAYESIAN METHOD; DISCRIMINANT ANALYSIS; SINGLE-GAUSSIAN STRUCTURE; TWO-GAUSSIAN STRUCTURE;

RADIAL BASIS FUNCTION NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; DISCRIMINANT ANALYSIS; INFORMATION RETRIEVAL; METHODOLOGY; SYSTEMS THEORY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DISCRIMINANT ANALYSIS; INFORMATION STORAGE AND RETRIEVAL; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; SYSTEMS THEORY;

EID: 33646500527     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2006.873282     Document Type: Article

References (43)

1. A. Alexandridis, P. Patrinos, H. Sarimveis, and G. Tsekouras, "A two-stage evolutionary algorithm for variable selection in the development of RBF neural network models," Chemo. Intell. Lab. Syst., vol. 75, no. 2, pp. 149-162, 2005.
2. P. L. Bartlett, "For valid generalization, the size of the weights is more important than the size of the network," in Adv. Neural Inf. Process. Syst., M. C. Mozer, M. I. Jordan, and T. Petsche, Eds. Cambridge, MA: MIT Press, 1997, vol. 9, pp. 134-140.
3. C. Bishop, Neural Networks for Pattern Recognition. London, U.K.: Oxford Univ. Press, 1995.
4. T. Chen and H. Chen, "Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks," IEEE Trans. Neural Netw., vol. 6, no. 4, pp. 904-910, Jul. 1995.
5. S. Chen, C.F. Cowan, and P. M. Grant, "Orthogonal least squares learning algorithm for radial basis function networks," IEEE Trans. Neural Netw., vol. 2, no. Mar., pp. 302-309, 1991.
6. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. Royal Statistical Soc. Series B, vol. 39, no. 1, pp. 1-38, 1977.
7. D. Du and D. Sun, "Comparison of three methods for classification of pizza topping using different color space transformations," J. Food Eng., vol. 68, no. 3, pp. 277-287, 2005.
8. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 2002.
9. S. Geman, E. Bienenstock, and R. Doursat, "Neural networks and the bias/variance dilemma," Neural Comput., vol. 4, no. 1, pp. 1-58, 1992.
10. T. Joachims, "Making large-scale SVM learning practical," in Advances in Kernel Methods - Support Vector Learning, B. Scholkopf and C. Burges, Eds. Cambridge, MA: MIT Press, 1999.
11. W. Kaminski and P. Strumillo, "Kernel orthonormalization in radial basis function neural networks," IEEE Trans. Neural Netw., vol. 8, no. 5, pp. 1177-1183, Sep. 1997.
12. N. B. Karayiannis and G. W. Mi, "Growing radial basis neural networks: Merging supervised and unsupervised learning with network growth techniques," IEEE Trans. Neural Netw., vol. 8, no. 6, pp. 1492-1506, Nov. 1997.
13. Q. Li, X. Chen, and Z. Hu, "Quantitative structure-property relationship studies for estimating boiling points of alcohols using calculated molecular descriptors with radial basis function neural networks," Chemometrics Intell. Lab. Syst., vol. 72, no. 1, pp. 93-100, 2004.
14. H. Liu, R. Zhang, X. Yao, M. Liu, Z. Hu, and B. Fan, "Prediction of electrophoretic mobility of substituted aromatic acids in different aqueous - Alcoholic solvents by capillary zone electrophoresis based on support vector machine," Analytica Chimica Acta, vol. 525, no. 1, pp. 31-41, 2004.
15. K. Z. Mao, "RBF neural network center selection based on Fisher ratio class separability measure," IEEE Trans. Neural Netw., vol. 13, no. 5, pp. 1211-1217, Sep. 2002.
16. J. Moody and C. Darken, "Learning with localized receptive fields," in Connectionist Models Summer School, T. Sejnowski, D. Touretzky, and G. Hinton, Eds. San Mateo, CA: Morgan Kaufman, 1988, pp. 133-143. Carnegie Mellon Univ.
17. J. Moody and C. Darken, "Fast learning in networks of locally tuned units," Neural Comput., vol. 1, no. 2, pp. 281-294, 1989.
18. Y. Oyang, S. Hwang, Y. Ou, C. Chen, and Z. Chen, "Data classification with radial basis function networks based on a novel kernel density estimation algorithm," IEEE Trans.Neural Netw., vol. 16, no. 1, pp. 225-236, Jan. 2005.
19. W. Pedrycz, "Conditional fuzzy clustering in the design of radial basis function neural networks," IEEE Trans. Neural Netw., vol. 9, no. 4, pp. 601-612, Jul. 1998.
20. T. Poggio and F. Girosi, "Networks for approximation and learning," in Proc. IEEE, vol. 78, Sep. 1990, pp. 1481-1497.
21. B. D. Ripley, Pattern Recognition and Neural Networks. Cambridge, MA: Cambridge Univ. Press, 1996.
22. V. M. Rivas, J. J. Merelo, P. A. Castillo, M. G. Arenas, and J. G. Castellano, "Evolving RBF neural networks for time-series forecasting with EvRBF," Inf. Sci., vol. 165, no. 3-4, pp. 207-220, 2004.
23. R. J. Schilling, J. J. Carroll, and A. F. Al-Ajlouni, "Approximation of nonlinear systems with radial basis function neural networks," IEEE Trans. Neural Netw., vol. 12, no. 1, pp. 1-15, Jan. 2001.
24. J. Q. Shi, R. Murray-Smith, and D. M. Titterington, "Bayesian regression and classification using mixture of Gaussian processes," Int. J. Adapt. Control Signal Process., vol. 17, no. 2, pp. 149-161, 2003.
25. V. G. Sigillito, S. P. Wing, L. V. Hutton, and K. B. Baker, "Classification of radar returns from the ionosphere using neural networks," Johns Hopkins APL Tech. Digest, vol. 10, no. 3, pp. 262-266, 1989.
26. J. W. Smith, J. E. Everhart, W. C. Dickson, W. C. Knowler, and R. S. Johannes, "Using the ADAP learning algorithm to forecast the onset of diabetes mellitus," in Proc. IEEE Symp. Computer Applications and Medical Care, 1988, pp. 261-265.
27. W. N. Street, W. H. Wolberg, and O. L. Mangasarian, "Nuclear feature extraction for breast tumor diagnosis," in IS & T/SPIE 1993 Int. Symp. Electronic Imaging: Science and Technology, vol. 1905, San Jose, CA, 1993, pp. 861-870.
28. P. Sykacek, "On input selection with reversible jump Markov chain Monte Carlo sampling," Advances in Neural Information Processing Systems, vol. 12, pp. 638-644, 2000.
29. R. Thomson, C. Hodgman, Z. R. Yang, and A. K. Doyle, "Characterising proteolytic cleavage site activity using bio-basis function neural network," Bioinformatics, vol. 21, pp. 1741-1747, 2003.
30. M. E. Tipping, "The relevance vector machine," Adv. Neural Inf. Process. Syst., vol. 12, pp. 652-658, Nov. 2000.
31. N. W. Townsend and L. Tarassenko, "Estimations of error bounds for neural-network function approximators," IEEE Trans. Neural Netw., vol. 10, no. 2, pp. 217-230, Mar. 1999.
32. V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
33. S. Wang, C. Xue, X. Chen, M. Liu, and Z. Hu, "Study on the quantitative relationship between the structures and electrophoretic mobilities of flavonoids in micellar electrokinetic capillary chromatography," J. Chromatography, vol. 1033, no. 1, pp. 153-159, 2004.
34. A. Weigend, D. E. Rumelhart, and B. A. Huberman, "Generalization by weight-elimination with application to forecasting," Advances in Neural Information Processing Systems, vol. 3, 1991.
35. C.K.I. Williams and D. Barber, "Bayesian Classification With Gaussian Processes," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 12, pp. 1342-1351, Dec. 1998.
36. N. Xie and H. Leung, "Blind equalization using a predictive radial basis function neural network," IEEE Trans. Neural Netw., vol. 16, no. 3, pp. 709-720, May 2005.
37. Z. R. Yang, S. Chen, and H. James, "Robust maximum likelihood training of probabilistic neural networks," Neural Netw., vol. 11, no. 4, pp. 739-747, 1998.
38. Z. R. Yang and K. C. Chou, "Bio-basis function neural networks for the prediction of the O-linkage sites in glyco-proteins," Bioinformatics, vol. 20, no. 5, pp. 903-908, 2004.
39. Z. R. Yang and K. C. Chou Thomson, "Bio-support vector machines for computational proteomics," Bioinformatics, vol. 20, no. 5, pp. 735-741, 2004.
40. Z. R. Yang and R. Thomson, "Bio-basis function neural network for prediction of protease cleavage sites in proteins," IEEE Trans. Neural Netw., vol. 16, no. 1, pp. 263-274, Jan. 2005.
41. Z. R. Yang, "Orthogonal kernel machine in prediction of functional sites in proteins," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 35, no. Feb., pp. 100-106, 2005.
42. X. J. Yao, A. Panaye, J. P. Doucet, H. F. Chen, R. S. Zhang, B. T. Fan, M. C. Liu, and Z. D. Hu, "Comparative classification study of toxicity mechanisms using support vector machines and radial basis function neural networks," Analytica Chimica Acta, vol. 535, no. 1-2, pp. 259-273, 2005.
43. W. Zheng and C. Zou, "Foley-Sammon optimal discriminant vectors using kernel approach," IEEE Trans. Neural Netw., vol. 16, no. 1, pp. 1-9, Jan. 2005.