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.
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Kůrková, V. (2012). Some Comparisons of Networks with Radial and Kernel Units. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_3
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DOI: https://doi.org/10.1007/978-3-642-33266-1_3
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