Reducing generalization error and category proliferation in ellipsoid ARTMAP via tunable misclassification error tolerance: Boosted ellipsoid ARTMAP

Georgios C. Anagnostopoulos, Michael Georgiopoulos, Stephen J. Verzi, Gregory L. Heileman

Research output: Contribution to conferencePaperpeer-review

18 Scopus citations

Abstract

In this paper we introduce Boosted Ellipsoid ARTMAP (bEAM), a variant of Ellipsoid ARTMAP, which via a tunable misclassification error tolerance increases the network's resubstitution error and implicitly performs structural risk minimization. bEAM constitutes another example of how modifications to Fuzzy ARTMAP can be naturally extended to Ellipsoid ARTMAP.

Original languageEnglish (US)
Pages2650-2655
Number of pages6
StatePublished - 2002
Externally publishedYes
Event2002 International Joint Conference on Neural Networks (IJCNN'02) - Honolulu, HI, United States
Duration: May 12 2002May 17 2002

Conference

Conference2002 International Joint Conference on Neural Networks (IJCNN'02)
Country/TerritoryUnited States
CityHonolulu, HI
Period5/12/025/17/02

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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