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We present a novel application of Bayesian techniques to Radial Basis Function networks by developing a Gaussian approximation to the posterior distribution ...
We present a novel application of Bayesian techniques to Radial Basis Function networks by developing a Gaussian approximation to the posterior distribution.
A novel application of Bayesian techniques to Radial Basis Function networks is presented by developing a Gaussian approximation to the posterior ...
Dec 1, 1997 · We present a novel application of Bayesian techniques to Radial Basis Function networks by developing a Gaussian approximation to the posterior ...
A Bayesian framework for the analysis of radial basis functions (RBF) is proposed that accommodates uncertainty in the dimension of the model.
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Abstract: We consider a fully Bayesian treatment of radial basis function regression, and propose a solution to the the instability of basis selection.
This approach allows the quantification of error bars or inference regions on the solution. The subject of inference regions in the context of Bayesian spline ...
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May 15, 2024 · In this paper we present a new fast and accurate method for Radial Basis Function (RBF) approximation, including interpolation as a special case.
We proposed and analyzed the Bayesian trained radial basis function (RBF) neural network in fMRI data processing. The method, which determines the ...
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We propose a hierarchical full Bayesian model for radial basis networks. This model treats the model dimension (number of neurons), model pa-.