Evolutionary Learning of Regularization Networks with Multi-kernel Units

0369159 - ÚI 2012 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Vidnerová, Petra - Neruda, Roman
Evolutionary Learning of Regularization Networks with Multi-kernel Units.
Advances in Neural Networks – ISNN 2011. Part I. Berlin: Springer, 2011 - (Liu, D.; Zhang, H.; Polycarpou, M.; Alippi, C.; He, H.), s. 538-546. Lecture Notes in Computer Science, 6675. ISBN 978-3-642-21104-1. ISSN 0302-9743.
[ISNN 2011. International Symposium on Neural Networks /8./. Guilin (CN), 29.05.2011-01.06.2011]
Grant CEP: GA ČR GAP202/11/1368
Výzkumný záměr: CEZ:AV0Z10300504
Klíčová slova: genetic algorithms * kernel functions * regularization networks
Kód oboru RIV: IN - Informatika

Regularization networks represent an important supervised learning method applicable for regression and classification tasks. They benefit from very good theoretical background, although the presence of meta parameters is their drawback. The meta parameters, including the type of kernel function, are typically supposed to be given in advance and come ready as an input of the algorithm. In this paper, we propose multi-kernel functions, namely product kernel functions and composite kernel functions. The choice of kernel function becomes part of the optimization process, for which a new evolutionary learning algorithm is introduced that deals with different kernel functions, including composite kernels. The results are demonstrated on experiments with benchmark tasks.
Trvalý link: http://hdl.handle.net/11104/0203294