Abstract
Clustering techniques have a strong influence on the performance achieved by Radial Basis Function (RBF) networks. This article compares the performance achieved by RBF networks using seven different clustering techniques. For such, different sizes of RBF networks are trained and tested using an Automatic Target Recognition data set. The performances of these RBF networks using each clustering technique are compared and analyzed. This article also evaluates how the performance can be improved by combining RBF networks, training with different clustering techniques, in committees.
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References
Broomhead D. S. and Lowe, D.: Multivariable Functional Interpolation and Adaptive Networks, Complex Systems 2 (1988), 321–355.
Chen, S.: Nonlinear time series modeling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning, Electronic Letters 31(2) (1995), 117–118.
Chinrungrueng, C. and Séquin, C. H.: Optimal Adaptive K-means Algorithm with Dynamic Adjustment of Learning Rate, IEEE Transactions on Neural Networks 6 (1995), 157–169.
Duda, R. O. and Hart, P. E.: Pattern Classification and Scene Analysis, Wiley-Interscience Publication, New York, 1973.
Gama, J. and Brazdil, P.: Characterization of Classiccation Algorithms. 7th Portuguese Conference on Artificial Intelligence, EPIA-95, Lecture Notes of Artificial Intelligence, pp. 189–200, 1995.
Haykin, S.: Neural Networks: A Comprehensive Foundation, Prentice Hall, New Jersey, 1999.
Ho, T., Hull, J. and Srihari, S.: Decision Combination in Multiple Classicer Systems, IEEE Transactions on Pattern Analysis Machine Intelligence 16(1) (1994), 66–75.
Ismail, M. A. and Kamel, M. S.: Multidimensional Data Clustering Utilizing Hybrid Search Strategies, Pattern Recognition 22 (1989), 75–89.
Ismail, M. A., Selim, S. Z. and Arora, S. K.: Efficient Clustering of Multidimensional data, Proceedings of the IEEE International Conference on Systems Man and Cybernetics, pp. 120–123, 1984.
Kim, D. S. and Lee, S. Y.: Intelligent Judge neural Network for speech recognition, Neural processing Letters 1 (1994), 17–20.
Lacerda, E. and Carvalho, A.: Credit analysis using radial basis function networks, Proceedings of the 3rd International Conference on Computational Intelligence and Multimedia Applications, ICCIMA'99, New Deli, India, IEEE Computer Press, pp. 138–142, 1999.
Lu, Y.: Knowledge Integrations in a Multiple Classicer System, Applied Intelligence 1 (1996), 75–86.
Lloyd, S. P.: Least Square quantization in PCM, IEEE Transactions on Information Theory 28(2) (1982), 129–137.
MacQueen, J.: Some Methods for Classiccation and Analysis of Multivariate Observations, Proceedings of the Fifth Berkley Symposium Math. Stat. Prob 1 (1967), 281–297.
Mak, M., Allen, W. and Sexton, G.: Speaker identiccation using multilayer perceptrons and radial basis functions networks, Neurocomputing 6 (1994), 99–117.
Moody, J. and Darken, C. J.: Fast learning in networks of locally-tuned processing units, Neural Computation, 1(2) (1989), 281–294.
Orr, M.: 1996, Introduction to radial basis function networks, Technical Report, Centre for Cognitive Science, University of Edinburgh, Scotland, (www.cns.ed.ac.uk/people/mark).
Powell, M.: The theory of radial basis function approximation in 1990'. Light, W. (ed.), Advances in Numerical Analysis, 3 (1992), 105–210, Oxford, Clarendon.
Prampero, P. and Carvalho, A.: Classicer Combination for Vehicle Silhouettes Recognition, Seventh International Conference on Image Processing and its Applications, IPA'99, Manchester, pp. 67–71, 1999.
Prechelt, L.: 1994, Proben1 - A Set of Neural Networks Benchmark Problems and Benchmarking Rules', Fakultät füt Informatik. Universität Karlsruhe, Technical Report 21/94.
Rahman, A. and Fairhurst, M. C.: Generalised approach to the recognition of structurally similar handwritten characters using multiple expert classicers, IEE Proc. Vision, Image and Signal Proc. 144 (1997), 15–22.
Rumelhart, D. E., Hinton, G. E. and Williams, R. J.: Learning internal representation by error propagation, In Parallel Distributed Processing, Cambridge, MIT Press, pp. 318–362, 1986.
Saha, A., Christian, J., Tang, D. and Wu, C.: Oriented Non-Radial Basis Functions for Image Coding and Analysis, Neural Information Processing Systems, pp. 728–734, 1991.
Selim, Z. and Ismail, M. A.: K-means-Type Algorithms: a generalized convergence theorem and characterization of local optimality, IEEE Transactions on Pattern Analysis Machine Intelligence, PAMI 6(1) (1984), 81–87.
Sousa, H. and Carvalho, A.: Pattern Recognition Using Constructive Algorithms, Proceedings of the International Joint Conference on Neural Networks, IJCNN'99, (CD-media), Washington, USA, 1999.
Sharkey, J.: On Combining Articcial Neural Nets, Connection Science 8 (1997), 299–313.
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de Carvalho, A., Brizzotti, M.M. Combining RBF Networks Trained by Different Clustering Techniques. Neural Processing Letters 14, 227–240 (2001). https://doi.org/10.1023/A:1012703414861
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DOI: https://doi.org/10.1023/A:1012703414861