Hyper basis function network: Difference between revisions

Revision as of 05:40, 21 December 2014

Revision as of 18:37, 26 December 2014

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In [[machine learning]], a '''Hyper basis function network''', or '''HyperBF network''', is a generalization of [[Radial basis function network|radial basis function (RBF) networks]] concept, where the [[Mahalanobis distance|Mahalanobis]]-like distance is used instead of Euclidian distance measure. Hyper basis function networks were first consideredintroduced by Poggio and Girosi in the 1990 at the paper “Networks for Approximation and Learning” <ref name="PoggioGirosi1990"> T. Poggio and F. Girosi (1990). "Networks for Approximation and Learning". ''Proc. of the IEEE'' '''Vol. 78, No. 9''':1481-1497.</ref><ref name="Mahdi">R.N. Mahdi, E.C. Rouchka (2011). [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5733426 "Reduced HyperBF Networks: Regularization by Explicit Complexity Reduction and Scaled Rprop-Based Training"]. ''IEEE Transactions of Neural Networks'' '''2''':673–686.</ref>.

==Network Architecture==

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==References==

{{reflist}}

[[Category:Artificial neural networks]]

[[Category:Classification algorithms]]

[[Category:Machine learning algorithms]]