Name
Title | ||
|---|---|---|
Gaussian ARTMAP: a neural network for fast incremental learning of noisy multidimensional maps |
Abstract | ||
|---|---|---|
A new neural network architecture for incremental supervised learning of analog multidimensional maps is introduced. The architecture, called Gaussian ARTMAP, is a synthesis of a Gaussian classifier and an adaptive resonance theory (ART) neural network, achieved by defining the ART choice function as the discriminant function of a Gaussian classifier with separable distributions, and the ART match function as the same, but with the distributions normalized to a unit height. While Gaussian ARTMAP retains the attractive parallel computing and fast learning properties of fuzzy ARTMAP, it learns a more efficient internal representation of a mapping while being more resistant to noise than fuzzy ARTMAP on a number of benchmark databases. Several simulations are presented which demonstrate that Gaussian ARTMAP consistently obtains a better trade-off of classification rate to number of categories than fuzzy ARTMAP. Results on a vowel classification problem are also presented which demonstrate that Gaussian ARTMAP outperforms many other classifiers. Copyright (C) 1996 Elsevier Science Ltd |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1016/0893-6080(95)00115-8 | Neural Networks |
Keywords | DocType | Volume |
pattern recognition,discriminant function,adaptive resonance theory,neural network,parallel computer,radial basis function,self organization,choice function,supervised learning | Journal | 9 |
Issue | ISSN | Citations |
5 | 0893-6080 | 153 |
PageRank | References | Authors |
8.00 | 9 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| James R. Williamson | 1 | 389 | 31.64 |