Evolutionary learning of regularization networks with product kernel units

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...

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Bibliographic Details
Main Authors: Vidnerova, P., Neruda, R.
Format: Conference Proceeding
Language:English
Subjects:
Approximation methods
Genetic algorithms
Genetics
Kernel
Kernel functions
Regularization networks
Testing
Training
Vectors
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Summary: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.
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2011.6083783