Regularization theory and neural networks architectures
Neural Computation
An introduction to genetic algorithms
An introduction to genetic algorithms
A Theory of Networks for Approximation and Learning
A Theory of Networks for Approximation and Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Sum and product kernel regularization networks
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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In this paper we propose a novel evolutionary algorithm for regularization networks. The main drawback of regularization networks in practical applications is the presence of meta-parameters, including the type and parameters of kernel functions Our learning algorithm provides a solution to this problem by searching through a space of different kernel functions, including sum and composite kernels. Thus, an optimal combination of kernel functions with parameters is evolved for given task specified by training data. Comparisons of composite kernels, single kernels, and traditional Gaussians are provided in several experiments.