In machine learning, a Hyper basis function network, or HyperBF network, is a generalization of radial basis function (RBF) networks concept, where the Mahalanobis-like distance is used instead of Euclidean distance measure. Hyper basis function networks were first introduced by Poggio and Girosi in the 1990 paper “Networks for Approximation and Learning”.[1][2]
^T. Poggio and F. Girosi (1990). "Networks for Approximation and Learning". Proc. IEEEVol. 78, No. 9:1481-1497.
^R.N. Mahdi, E.C. Rouchka (2011). "Reduced HyperBF Networks: Regularization by Explicit Complexity Reduction and Scaled Rprop-Based Training". IEEE Transactions of Neural Networks2:673–686.
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