Add to ORKGThe Radial Basis Function (RBF) neural networks are nonparametric regression tools similar in formula to the Nadaraya-Watson kernel estimator. Starting from this resemblance, the relationships between the RBF neural nets and the kernel methods are discussed. Some results regarding the moment matrix of the radial basis functions are used to understand the linkage between these estimators and to show a different behaviour of their bandwidth. It is also recalled that the rate of convergence of RBF nets is not dependent on the dimension of the domain of the regression function unlike kernel methods. Afterwards the comparison moves on more applied aspects and a RBF net joint on an ARCH model is proposed to analyse financial time series
Originally, artificial neural networks were built from biologically inspired units called perceptron...
The research presented in this dissertation offers an extension to the classic Broomhead and Lowe Ra...
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks....
The Radial Basis Function (RBF) neural networks are nonparametric regression tools similar in formul...
The radial basis function (RBF) network has been used intensively. Besides its applications, several...
This study explores the learning problem from two broad perspectives, consisting of statistical regr...
Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have sh...
In this paper we present the Radial Basis Neural Network Function. We examine some simple numerical ...
Useful connections between radial basis function (RBF) nets and kernel regression estimators (KRE) a...
A look is taken at the use of radial basis functions (RBFs), for nonlinear system identification. RB...
After the introduction to neural network technology as multivariable function approximation, radial ...
Summarization: Financial management maximise investors’ return, seeking for stocks with increasing e...
In this paper different methods for training radial basis function (RBF) networks for regression pro...
<p>This figure illustrates the model of the RBFNN. The network has three layers: the input layer, th...
Le reti neurali sono modelli di regressione non parametrica interessanti per la loro efficacia in pr...
Originally, artificial neural networks were built from biologically inspired units called perceptron...
The research presented in this dissertation offers an extension to the classic Broomhead and Lowe Ra...
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks....
The Radial Basis Function (RBF) neural networks are nonparametric regression tools similar in formul...
The radial basis function (RBF) network has been used intensively. Besides its applications, several...
This study explores the learning problem from two broad perspectives, consisting of statistical regr...
Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have sh...
In this paper we present the Radial Basis Neural Network Function. We examine some simple numerical ...
Useful connections between radial basis function (RBF) nets and kernel regression estimators (KRE) a...
A look is taken at the use of radial basis functions (RBFs), for nonlinear system identification. RB...
After the introduction to neural network technology as multivariable function approximation, radial ...
Summarization: Financial management maximise investors’ return, seeking for stocks with increasing e...
In this paper different methods for training radial basis function (RBF) networks for regression pro...
<p>This figure illustrates the model of the RBFNN. The network has three layers: the input layer, th...
Le reti neurali sono modelli di regressione non parametrica interessanti per la loro efficacia in pr...
Originally, artificial neural networks were built from biologically inspired units called perceptron...
The research presented in this dissertation offers an extension to the classic Broomhead and Lowe Ra...
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks....