Abstract
In this paper we propose a novel evolutionary algorithm for regularization networks. The main drawback of regularization networks in practical applications is the presence of meta-parameters, including the type and parameters of kernel functions Our learning algorithm provides a solution to this problem by searching through a space of different kernel functions, including sum and composite kernels. Thus, an optimal combination of kernel functions with parameters is evolved for given task specified by training data. Comparisons of composite kernels, single kernels, and traditional Gaussians are provided in several experiments.
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References
Girosi, F., Jones, M., Poggio, T.: Regularization theory and Neural Networks architectures. Neural Computation 2, 219–269 (1995)
Kůrková, V.: Learning from data as an inverse problem. In: Antoch, J. (ed.) Computational Statistics, pp. 1377–1384. Physica Verlag, Heidelberg (2004)
Poggio, T., Girosi, F.: A theory of networks for approximation and learning. Technical report, Cambridge, MA, USA (1989); A. I. Memo No. 1140, C.B.I.P. Paper No. 31
Poggio, T., Smale, S.: The mathematics of learning: Dealing with data. Notices of the AMS 50, 536–544 (2003)
Neruda, R., Vidnerová, P.: Genetic algorithm with species for regularization network metalearning. In: Papasratorn, B., Lavangnananda, K., Chutimaskul, W., Vanijja, V. (eds.) Advances in Information Technology. Communications in Computer and Information Science, vol. 114, pp. 192–201. Springer, Heidelberg (2010)
Aronszajn, N.: Theory of reproducing kernels. Transactions of the AMS 68, 337–404 (1950)
Kudová, P., Šámalová, T.: Sum and product kernel regularization networks. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 56–65. Springer, Heidelberg (2006)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Prechelt, L.: PROBEN1 – a set of benchmarks and benchmarking rules for neural network training algorithms. Technical Report 21/94, Universitaet Karlsruhe (9 (1994)
LAPACK: Linear algebra package, http://www.netlib.org/lapack/
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
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Vidnerová, P., Neruda, R. (2011). Evolving Sum and Composite Kernel Functions for Regularization Networks. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20282-7_19
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DOI: https://doi.org/10.1007/978-3-642-20282-7_19
Publisher Name: Springer, Berlin, Heidelberg
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