Rates of approximation by networks with Gaussian RBFs with varying widths are investigated. For certain smooth functions, upper bounds are derived in terms of a Sobolev-equivalent ...
In this paper we focus on an interpretation of Gaussian radial basis functions (GRBF) which motivates extensions and learning strategies. Specifically, we show that GRBF regressio...
— We consider nonlinear detection in rank-deficient multiple-antenna assisted beamforming systems. By exploiting the inherent symmetry of the underlying optimal Bayesian detecti...
Abstract. Radial basis function (RBF) approximation is an extremely powerful tool for representing smooth functions in non-trivial geometries, since the method is meshfree and can ...
In this paper we investigate alternative designs of a Radial Basis Function Network acting as classifier in a face recognition system. Input to the RBF network is the projections ...
Carlos E. Thomaz, Raul Queiroz Feitosa, Alvaro Vei...