Abstract
Supervised Adaptive Resonance Theory is a family of neural networks that performs incremental supervised learning of recognition categories (pattern classes) and multidimensional maps of both binary and analog patterns. This chapter highlights that the supervised ART architecture is compatible with IF-THEN rule-based symbolic representation. Specifically, the knowledge learned by a supervised ART system can be readily translated into rules for interpretation. Similarly, a priori domain knowledge in the form of IF-THEN rules can be converted into a supervised ART architecture. Not only does initializing networks with prior knowledge improve predictive accuracy and learning efficiency, the inserted symbolic knowledge can also be refined and enhanced by the supervised ART learning algorithm. By preserving symbolic rule form during learning, the rules extracted from a supervised ART system can be compared directly with the originally inserted rules.
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Tan, AH. (2000). Supervised Adaptive Resonance Theory and Rules. In: Jain, L.C., Lazzerini, B., Halici, U. (eds) Innovations in ART Neural Networks. Studies in Fuzziness and Soft Computing, vol 43. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1857-4_4
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DOI: https://doi.org/10.1007/978-3-7908-1857-4_4
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