Hyper basis function network: Difference between revisions

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Revision as of 10:37, 7 April 2015

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{{Orphan|date=December 2014}}

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 EuclidianEuclidean distance measure. Hyper basis function networks were first introduced by Poggio and Girosi in the 1990 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==