Some Comparisons of Networks with Radial and Kernel Units

Authors: Věra Kůrková
Publication year: 2012
Publication type: Konferenční příspěvek (mezinárodní konf.)
Periodical: Artificial Neural Networks and Machine Learning – ICANN 2012
Projects: Learning of functional relationships from high-dimensional data
Bibliographic citation: KŮRKOVÁ, Věra. 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. Berlin: Springer, 2012, S. 17-24. Lecture Notes in Computer Science, 7553. ISBN 978-3-642-33265-4. ISSN 0302-9743
Dostupné z: doi:10.1007/978-3-642-33266-1_3
DOI: 10.1007/978-3-642-33266-1_3
ID: 379010

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.