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Diffuse pattern learning with Fuzzy ARTMAP and PASS

  • Jorge Muruzábal
  • Alberto Muñoz
Comparison of Different Evolutionary Algorithms
Part of the Lecture Notes in Computer Science book series (LNCS, volume 866)

Abstract

Fuzzy ARTMAP is compared to a classifier system (CS) called PASS (predictive adaptive sequential system). Previously reported results in a benchmark classification task suggest that Fuzzy ARTMAP systems perform better and are more parsimonious than systems based on the CS architecture. The tasks considered here differ from ordinary classificatory tasks in the amount of output uncertainty associated with input categories. To be successful, learning systems must identify not only correct input categories, but also the most likely outputs for those categories. Performance under various types of diffuse patterns is investigated using a simulated scenario.

Keywords

Output Space Adaptive Resonance Theory Natural Code Fuzzy ARTMAP Output Uncertainty 
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 1994

Authors and Affiliations

  • Jorge Muruzábal
    • 1
  • Alberto Muñoz
    • 2
  1. 1.Department of Statistics and EconometricsUniversity Carlos IIIGetafeSpain
  2. 2.Department of Applied PhysicsUniversity of SalamancaSalamancaSpain

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