Abstract
Extreme learning machines or radial basis function networks depends on kernel functions. If the kernel set is too small or not adequate (for the problem/learning data) the learning can be fruitless and generalization capabilities of classifiers do not become rewarding.
The article presents a method of automatic stochastic selection of kernels. Thanks to the proposed scheme of kernel function selection we obtain the proper number of kernels and proper placements of kernels. Evaluation results clearly show that this methodology works very well and is superior to standard extreme learning machine, support vector machine or k nearest neighbours method.
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Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: International Joint Conference on Neural Networks, pp. 985–990. IEEE Press (2004)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)
Broomhead, D.S., Lowe, D.: Multivariable functional interpolation and adaptive networks. Complex Syst. 2, 321–355 (1988)
Kasun, L.L.C., Zhou, H., Huang, G.B.: Representational learning with ELMS for big data. IEEE Intell. Syst. 28(6), 31–34 (2013)
Jankowski, N., Grochowski, M.: Comparison of instances seletion algorithms I. Algorithms survey. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 598–603. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24844-6_90
Grochowski, M., Jankowski, N.: Comparison of instance selection algorithms II. Results and comments. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 580–585. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24844-6_87
Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithms. Mach. Learn. 38, 257–286 (2000)
Merz, C.J., Murphy, P.M.: UCI repository of machine learning databases (1998). http://www.ics.uci.edu/~mlearn/MLRepository.html
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Jankowski, N. (2018). Prototype-Based Kernels for Extreme Learning Machines and Radial Basis Function Networks. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_7
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DOI: https://doi.org/10.1007/978-3-319-91253-0_7
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