ARTMAP: a self-organizing neural network architecture for fast supervised learning and pattern recognition

The authors present a neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of adaptive resonance theory modules that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. Tested on a benchmark machine learning database in both online and offline simulations, the ARTMAP system learned orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations