Supervised Adaptive Resonance Theory and Rules

  • A.-H. Tan
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 43)


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.


Choice Function Category Node Confidence Factor Category Choice Fuzzy ARTMAP 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • A.-H. Tan
    • 1
  1. 1.Kent Ridge Digital LabsNational University of SingaporeSingapore

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