
General Regression Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, and Feedforward Neural Networks
The aim of this project is to develop a code to discover the optimal sig...
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Shrinkage priors for nonparametric Bayesian prediction of nonhomogeneous Poisson processes
We consider nonparametric Bayesian estimation and prediction for nonhomo...
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Face Detection Using Radial Basis Functions Neural Networks With Fixed Spread
This paper presented a face detection system using Radial Basis Function...
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An Exact Reformulation of FeatureVectorbased RadialBasisFunction Networks for Graphbased Observations
Radialbasisfunction networks are traditionally defined for sets of vec...
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Nonparametric Bayesian analysis of the compound Poisson prior for support boundary recovery
Given data from a Poisson point process with intensity (x,y) n 1(f(x)≤ ...
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A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module
The design process of photovoltaic (PV) modules can be greatly enhanced ...
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LSSVR as a Bayesian RBF network
We show the theoretical equivalence between the Least Squares Support Ve...
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Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks
While Bayesian neural networks have many appealing characteristics, current priors do not easily allow users to specify basic properties such as expected lengthscale or amplitude variance. In this work, we introduce Poisson Process Radial Basis Function Networks, a novel prior that is able to encode amplitude stationarity and inputdependent lengthscale. We prove that our novel formulation allows for a decoupled specification of these properties, and that the estimated regression function is consistent as the number of observations tends to infinity. We demonstrate its behavior on synthetic and real examples.
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