Add to ORKGThe radial basis function (RBF) network has been used intensively. Besides its applications, several theoretical results have been obtained. RBF networks can be naturally derived from regularization theory. In this paper, connections between RBF networks and the kernel regression estimator are considered.EI
Abstract:- The radial basis function (RBF) network is the main practical alternative to the multi-la...
Originally, artificial neural networks were built from biologically inspired units called perceptron...
This work investigates learning and generalisation capabilities of Radial Basis Function Networks us...
Useful connections between radial basis function (RBF) nets and kernel regression estimators (KRE) a...
The Radial Basis Function (RBF) neural networks are nonparametric regression tools similar in formul...
This study explores the learning problem from two broad perspectives, consisting of statistical regr...
In this paper different methods for training radial basis function (RBF) networks for regression pro...
Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have sh...
In this work we study and develop learning algorithms for networks based on regularization theory. I...
The present study employs an idea of mapping data into a high dimensional feature space which is kno...
After the introduction to neural network technology as multivariable function approximation, radial ...
Abstract — Function approximation has been found in many applications. The radial basis function (RB...
Radial Basis Function (RBF) networks are examples of a versatile artificial neural network paradigm ...
In this paper we propose a novel approach for modeling kernels in Radial Basis Function networks. Th...
We present a dual structural radial basis function (RBF) network for recursive function estimation. ...
Abstract:- The radial basis function (RBF) network is the main practical alternative to the multi-la...
Originally, artificial neural networks were built from biologically inspired units called perceptron...
This work investigates learning and generalisation capabilities of Radial Basis Function Networks us...
Useful connections between radial basis function (RBF) nets and kernel regression estimators (KRE) a...
The Radial Basis Function (RBF) neural networks are nonparametric regression tools similar in formul...
This study explores the learning problem from two broad perspectives, consisting of statistical regr...
In this paper different methods for training radial basis function (RBF) networks for regression pro...
Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have sh...
In this work we study and develop learning algorithms for networks based on regularization theory. I...
The present study employs an idea of mapping data into a high dimensional feature space which is kno...
After the introduction to neural network technology as multivariable function approximation, radial ...
Abstract — Function approximation has been found in many applications. The radial basis function (RB...
Radial Basis Function (RBF) networks are examples of a versatile artificial neural network paradigm ...
In this paper we propose a novel approach for modeling kernels in Radial Basis Function networks. Th...
We present a dual structural radial basis function (RBF) network for recursive function estimation. ...
Abstract:- The radial basis function (RBF) network is the main practical alternative to the multi-la...
Originally, artificial neural networks were built from biologically inspired units called perceptron...
This work investigates learning and generalisation capabilities of Radial Basis Function Networks us...