Genetic Algorithm with Species for Regularization Network Metalearning

0356026 - ÚI 2011 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Neruda, Roman - Vidnerová, Petra
Genetic Algorithm with Species for Regularization Network Metalearning.
Advances in Information Technology. Berlin: Springer, 2010 - (Papasratorn, B.; Lavangnananda, K.; Chutimaskul, W.; Vanijja, V.), s. 192-201. Communications in Computer and Information Science, 114. ISBN 978-3-642-16698-3. ISSN 1865-0929.
[IAIT 2010. International Conference on Advances in Information Technology /4./. Bangkok (TH), 04.11.2010-05.11.2010]
Grant CEP: GA ČR GA201/08/1744
Výzkumný záměr: CEZ:AV0Z10300504
Klíčová slova: regularization * neural networks * metalearning * genetic algorithms
Kód oboru RIV: IN - Informatika

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.
Trvalý link: http://hdl.handle.net/11104/0194655