Quantum speedup of training radial basis function networks

Hybrid generative/discriminative training of radial basis function networks

Efficient VLSI Architecture for Training Radial Basis Function Networks

This paper presents a novel VLSI architecture for the training of radial basis function (RBF) networks. The architecture contains the circuits for fuzzy C-means (FCM) and the recursive Least Mean Square (LMS) operations. The FCM circuit is designed for the training of centers in the hidden layer of the RBF network. The recursive LMS circuit is adopted for the training of connecting weights in t...

Training Radial Basis Function Networks by Genetic Algorithms

One of the issues of modeling a RBFNN Radial Basis Function Neural Network consists of determining the weights of the output layer, usually represented by a rectangular matrix. The inconvenient characteristic at this stage it’s the calculation of the pseudo-inverse of the activation values matrix. This operation may become computationally expensive and cause rounding errors when the amount of v...

Heterogeneous Radial Basis Function Networks

Radial Basis Function (RBF) networks typically use a distance function designed for numeric attributes, such as Euclidean or city-block distance. This paper presents a heterogeneous distance function which is appropriate for applications with symbolic attributes, numeric attributes, or both. Empirical results on 30 data sets indicate that the heterogeneous distance metric yields significantly i...

Functional radial basis function networks

There has been recently a lot of interest for functional data analysis [1] and extensions of well-known methods to functional inputs (clustering algorithm [2], non-parametric models [3], MLP [4]). The main motivation of these methods is to benefit from the enforced inner structure of the data. This paper presents how functional data can be used with RBFN, and how the inner structure of the form...