Add to ORKGThis study explores the learning problem from two broad perspectives, consisting of statistical regression estimation and RBF network formulation
Radial Basis Function networks with linear outputs are often used in regression problems because the...
Useful connections between radial basis function (RBF) nets and kernel regression estimators (KRE) a...
In this work we study and develop learning algorithms for networks based on regularization theory. I...
The radial basis function (RBF) network has been used intensively. Besides its applications, several...
Radial Basis Function (RBF) networks are examples of a versatile artificial neural network paradigm ...
In this paper a new, one step strategy for learning Radial Basis Functions network parameters is pro...
The Radial Basis Function (RBF) neural networks are nonparametric regression tools similar in formul...
In this paper different methods for training radial basis function (RBF) networks for regression pro...
The radial basis function (RBF) network offers a viable alternative to the two-layer neural network ...
This dissertation presents a new strategy for the automatic design of neural networks. The learning ...
Regularization Networks and Support Vector Machines are techniques for solv-ing certain problems of ...
This work investigates learning and generalisation capabilities of Radial Basis Function Networks us...
Abstract: We present various learning methods for RBF networks. The standard gradient-based learning...
This thesis describes the implementation of a Radial Basis Function (RBF) network to be used in pred...
Abstract:- The radial basis function (RBF) network is the main practical alternative to the multi-la...
Radial Basis Function networks with linear outputs are often used in regression problems because the...
Useful connections between radial basis function (RBF) nets and kernel regression estimators (KRE) a...
In this work we study and develop learning algorithms for networks based on regularization theory. I...
The radial basis function (RBF) network has been used intensively. Besides its applications, several...
Radial Basis Function (RBF) networks are examples of a versatile artificial neural network paradigm ...
In this paper a new, one step strategy for learning Radial Basis Functions network parameters is pro...
The Radial Basis Function (RBF) neural networks are nonparametric regression tools similar in formul...
In this paper different methods for training radial basis function (RBF) networks for regression pro...
The radial basis function (RBF) network offers a viable alternative to the two-layer neural network ...
This dissertation presents a new strategy for the automatic design of neural networks. The learning ...
Regularization Networks and Support Vector Machines are techniques for solv-ing certain problems of ...
This work investigates learning and generalisation capabilities of Radial Basis Function Networks us...
Abstract: We present various learning methods for RBF networks. The standard gradient-based learning...
This thesis describes the implementation of a Radial Basis Function (RBF) network to be used in pred...
Abstract:- The radial basis function (RBF) network is the main practical alternative to the multi-la...
Radial Basis Function networks with linear outputs are often used in regression problems because the...
Useful connections between radial basis function (RBF) nets and kernel regression estimators (KRE) a...
In this work we study and develop learning algorithms for networks based on regularization theory. I...