Abstract
Regularization networks are one of the important methods for supervised learning. They benefit from very good theoretical background, though their drawback is the presence of metaparameters. The metaparameters are typically supposed to be given by an user. In this paper, we develop a method for finding optimal values for metaparameters, namely type of kernel function, kernel’s parameter and regularization parameter. The method is based on genetic algorithms with different species for different kinds of kernels. The method is demonstrated on experiments.
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Neruda, R., Vidnerová, P. (2010). Genetic Algorithm with Species for Regularization Network Metalearning. In: Papasratorn, B., Lavangnananda, K., Chutimaskul, W., Vanijja, V. (eds) Advances in Information Technology. IAIT 2010. Communications in Computer and Information Science, vol 114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16699-0_21
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DOI: https://doi.org/10.1007/978-3-642-16699-0_21
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