Adaptive resonance theory (ART) models are being used for learning and prediction in a wide variety of applications. Winner-take-all coding allows these networks to maintain stable memories, but this type of code representation can cause problems such as category proliferation with fast learning and a noisy training set. A new class of ART models overcomes this limitation, permitting code repre...

The task how to predict profiles of potential customers for a product is important for a direct mailing company. A good prediction allows the company to detect potential customers and to avoid unnecessary and unwanted mailing. This paper describes a non-linear methodology to predict profiles of potential customers using Fuzzy ARTMAP neural networks. The paper discusses advantages of the propose...

In this paper, a novel on-line regression method is presented. Due to its origins in Adaptive Resonance Theory neural networks, this method is particularly well-suited to problems requiring stable incremental learning. Its performance on five publicly available datasets is shown to be at least comparable to two established off-line methods. Furthermore, it exhibits considerable improvements in ...

We propose a novel method to classify alcohol abusers. The method efficiently estimates total power in gamma band frequency (GBF) of multi-channel Visual Evoked Potential (VEP) signals in the time domain, circumventing power spectrum computation. These are used as features to classify alcohol abusers from control subjects with Multilayer PerceptronBackpropagation (MLP-BP) and Fuzzy ARTMAP (FA) ...

Neural network classifiers have been shown to provide supervised classification results that significantly improve on traditional classification algorithms such as the Bayesian (maximum likelihood [ML]) classifier. While the predominant neural network architecture has been the feedforward multilayer perceptron known as backpropagation, Adaptive resonance theory (ART) neural networks offer advan...

The presentation order of training patterns to a simplified fuzzy ARTMAP (SFAM) neural network affects the classification performance. The common method to solve this problem is to use several simulations with training patterns presented in random order, where voting strategy is used to compute the final performance. Recently, an ordering method based on min–max clustering was introduced to sel...

In this paper, we present recognition of handwritten characters of Arabic script. Arabic is now the 6 most spoken language in the world and is spoken by more than 200 million people worldwide. The 7 Century A.D., Arabic started to spread to the Middle East as many people started to convert to Islam. During this time of religious conversions, Arabic replaced many South Arabian languages, most of...

In this paper, we introduce a modification of the Fuzzy ARTMAP (FAM) neural network, namely, the Fuzzy ARTMAP with adaptively weighted distances (FAMawd) neural network. In FAMawd we substitute the regular L1-norm with a weighted L1-norm to measure the distances between categories and input patterns. The distance-related weights are a function of a category’s shape and allow for bias in the dir...