Some comparisons of networks with radial and kernel units

Authors:
Věra Kůrková
Affiliations:
Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Year:
2012

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Abstract

Two types of computational models, radial-basis function networks with units having varying widths and kernel networks where all units have a fixed width, are investigated in the framework of scaled kernels. The impact of widths of kernels on approximation of multivariable functions, generalization modelled by regularization with kernel stabilizers, and minimization of error functionals is analyzed.