Radial basis function networks with partially classified data

Computational Experience with Radial Basis Function Networks

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...

Radial Basis Function Networks – Revisited

T he radial basis function (RBF) network is an adaptive network model introduced by Broomhead and Lowe [1] which was motivated by the mathematics of approximating arbitrary continuous functions. This essay presents a personal recollection and reflection of key ideas in the introduction, motivation and evolution of the RBF network. The original article was slow to be taken up by the community, b...

Growing Radial Basis Function Networks

This paper presents and evaluates two algorithms for incrementally constructing Radial Basis Function Networks, a class of neural networks which looks more suitable for adtaptive control applications than the more popular backpropagation networks. The rst algorithm has been derived by a previous method developed by Fritzke, while the second one has been inspired by the CART algorithm developed ...