Name
Title | ||
|---|---|---|
Tackling the "Curse of Dimensionality" of Radial Basis Functional Neural Networks Using a Genetic Algorithm |
Abstract | ||
|---|---|---|
Radial Basis Function (RBF) neural networks offer the possibility of faster gradient-based learning of neuron weights compared with Multi-Layer Perceptron (MLP) networks. This apparent advantage of RBF networks is bought at the expense of requiring a large number of hidden layer nodes, particularly in high dimensional spaces (the curse of dimensionality). This paper proposes a representation and associated genetic operators which are capable of evolving RBF networks with relatively small numbers of hidden layer nodes and good generalisation properties. The genetic operators employed also overcome the competing conventions problem, for RBF networks at least, which has been a reported stumbling block in the application of crossover operators in evolutionary learning of directly encoded neural network architectures. |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1007/3-540-61723-X_1034 | PPSN |
Keywords | Field | DocType |
radial basis functional neural,genetic algorithm,multi layer perceptron,radial basis function,neural network,curse of dimensionality,genetic operator | Radial basis function network,Crossover,Pattern recognition,Computer science,Curse of dimensionality,Probabilistic neural network,Time delay neural network,Artificial intelligence,Artificial neural network,Perceptron,Machine learning,Genetic algorithm | Conference |
ISBN | Citations | PageRank |
3-540-61723-X | 3 | 0.45 |
References | Authors | |
11 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brian Carse | 1 | 259 | 26.31 |
| T C Fogarty | 2 | 1147 | 152.53 |