In this paper different methods for training radial basis function (RBF) networks for regression problems are described and illustrated. Then, using data from the DELVE archive, they are empirically compared with each other and with some other well known methods for machine learning. Each of the RBF methods performs well on at least one DELVE task, but none are as consistent as the best of the other non-RBF methods
The radial basis function (RBF) network offers a viable alternative to the two-layer neural network ...
The paper considers a number of strategies for training radial basis function (RBF) classifiers. A b...
Abstract: A novel technique is proposed for the incremental construction of sparse radial basis func...
The radial basis function (RBF) network has been used intensively. Besides its applications, several...
Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have sh...
This study explores the learning problem from two broad perspectives, consisting of statistical regr...
Radial Basis Function networks with linear outputs are often used in regression problems because the...
In this paper a new, one step strategy for learning Radial Basis Functions network parameters is pro...
Radial Basis Function (RBF) networks are examples of a versatile artificial neural network paradigm ...
Abstract: We present various learning methods for RBF networks. The standard gradient-based learning...
Abstract:- The radial basis function (RBF) network is the main practical alternative to the multi-la...
The problem of training a radial basis function (RBF) neural network for distinguishing two disjoint...
Radial basis function neural networks (RBF neural networks), as an alternative to multilayer percept...
The optimisation and adaptation of single hidden layer feed-forward neural networks employing radial...
The Radial Basis Function (RBF) neural networks are nonparametric regression tools similar in formul...
The radial basis function (RBF) network offers a viable alternative to the two-layer neural network ...
The paper considers a number of strategies for training radial basis function (RBF) classifiers. A b...
Abstract: A novel technique is proposed for the incremental construction of sparse radial basis func...
The radial basis function (RBF) network has been used intensively. Besides its applications, several...
Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have sh...
This study explores the learning problem from two broad perspectives, consisting of statistical regr...
Radial Basis Function networks with linear outputs are often used in regression problems because the...
In this paper a new, one step strategy for learning Radial Basis Functions network parameters is pro...
Radial Basis Function (RBF) networks are examples of a versatile artificial neural network paradigm ...
Abstract: We present various learning methods for RBF networks. The standard gradient-based learning...
Abstract:- The radial basis function (RBF) network is the main practical alternative to the multi-la...
The problem of training a radial basis function (RBF) neural network for distinguishing two disjoint...
Radial basis function neural networks (RBF neural networks), as an alternative to multilayer percept...
The optimisation and adaptation of single hidden layer feed-forward neural networks employing radial...
The Radial Basis Function (RBF) neural networks are nonparametric regression tools similar in formul...
The radial basis function (RBF) network offers a viable alternative to the two-layer neural network ...
The paper considers a number of strategies for training radial basis function (RBF) classifiers. A b...
Abstract: A novel technique is proposed for the incremental construction of sparse radial basis func...
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