Rademacher penalization applied to Fuzzy ARTMAP and Boosted ARTMAP

S. J. Verzi, G. L. Heileman, M. Georgiopoulos, M. J. Healy

Research output: Contribution to conferencePaperpeer-review

22 Scopus citations

Abstract

In our research we are interested in bounding the performance of Fuzzy ARTMAP and other ART-based neural network architectures, such as Boosted ARTMAP, according to the theory of Structural Risk Minimization. Structural risk minimization research indicates a trade-off between training error and hypothesis complexity. This trade-off directly motivated Boosted ARTMAP. In this paper, we present empirical evidence for Boosted ARTMAP as a viable learning technique, in general, in comparison to Fuzzy ARTMAP and other ART-based neural network architectures. We also show direct empirical evidence for decreased hypothesis complexity in conjunction with improved empirical performance for Boosted ARTMAP as compared with Fuzzy ARTMAP. Application of the Rademacher penalty to Boosted ARTMAP on a specific learning problem further indicates its utility as compared with Fuzzy ARTMAP.

Original languageEnglish (US)
Pages1191-1196
Number of pages6
StatePublished - 2001
Externally publishedYes
EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States
Duration: Jul 15 2001Jul 19 2001

Conference

ConferenceInternational Joint Conference on Neural Networks (IJCNN'01)
Country/TerritoryUnited States
CityWashington, DC
Period7/15/017/19/01

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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