Evolutionary learning of regularization networks with product kernel units
Petra Vidnerová, Roman Neruda · 2011
This paper deals with learning possibilities of regularization networks with product kernel units. Approximation problems formulated as regularized minimization problems with kernel-based stabilizers lead to solutions of the shape of linear combination of kernel functions. These can be expressed as one-hidden layer feed-forward neural network schemes, called regularization networks. We propose a novel evolutionary algorithm utilizing for regularization networks with product kernels. This algorithm utilizes genetic search for suitable network parameters as well as kernel functions.