Add to ORKGMedical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
After the introduction to neural network technology as multivariable function approximation, radial ...
The optimisation and adaptation of single hidden layer feed-forward neural networks employing radial...
Radial Basis Function (RBF) has been widely used in different fields, due to its fast learning and i...
Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have sh...
In this article an attempt is made to study the applicability of a general purpose, supervised feed...
In this paper we discuss the learning problem of Radial Basis Function (RBF) Neural Networks. We pro...
Radial basis function neural networks (RBF neural networks), as an alternative to multilayer percept...
Abstract—Radial basis function (RBF) networks have advan-tages of easy design, good generalization, ...
In this paper, a new variant of the Radial Basis Function Network with the Dynamic Decay Adjustment ...
Artificial neural networks are powerfultools for analysing information expressed as data sets, which...
Radial Basis Function (RBF) is a type of feed forward neural network .This function can be applied t...
[[abstract]]Feedforward neural networks have demonstrated an ability to learn arbitrary nonlinear ma...
Neural networks are family statistical learning algorithms and structures and are used to estimate o...
This paper proposes a novel learning algorithm for constructing data classifiers with radial basis f...
This dissertation presents a new strategy for the automatic design of neural networks. The learning ...
After the introduction to neural network technology as multivariable function approximation, radial ...
The optimisation and adaptation of single hidden layer feed-forward neural networks employing radial...
Radial Basis Function (RBF) has been widely used in different fields, due to its fast learning and i...
Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have sh...
In this article an attempt is made to study the applicability of a general purpose, supervised feed...
In this paper we discuss the learning problem of Radial Basis Function (RBF) Neural Networks. We pro...
Radial basis function neural networks (RBF neural networks), as an alternative to multilayer percept...
Abstract—Radial basis function (RBF) networks have advan-tages of easy design, good generalization, ...
In this paper, a new variant of the Radial Basis Function Network with the Dynamic Decay Adjustment ...
Artificial neural networks are powerfultools for analysing information expressed as data sets, which...
Radial Basis Function (RBF) is a type of feed forward neural network .This function can be applied t...
[[abstract]]Feedforward neural networks have demonstrated an ability to learn arbitrary nonlinear ma...
Neural networks are family statistical learning algorithms and structures and are used to estimate o...
This paper proposes a novel learning algorithm for constructing data classifiers with radial basis f...
This dissertation presents a new strategy for the automatic design of neural networks. The learning ...
After the introduction to neural network technology as multivariable function approximation, radial ...
The optimisation and adaptation of single hidden layer feed-forward neural networks employing radial...
Radial Basis Function (RBF) has been widely used in different fields, due to its fast learning and i...