Williamson, James R.2011-11-142011-11-141995-02https://hdl.handle.net/2144/2180A new neural network architecture for incremental supervised learning of analalog 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 classifer with separable distributions, and the ART match function as the same, but with the a priori probabilities of the distributions discounted. 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 classiflcation problem are also presented which demonstrate that Gaussian ARTMAP outperforms many other classifiers.en-USCopyright 1995 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.Pattern recognitionAdaptive Resonance Theory (ART)ARTMAPIncremental learningSelf-organizationNoisy dataGaussian classifierRadial basis functionTechnical ReportBoston University Trustees