The generalization capabilities of ARTMAP

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Bounds on the number of training examples needed to guarantee a certain level of generalization performance in the ARTMAP architecture are derived. Conditions are derived under which ARTMAP can achieve a specific level of performance assuming any unknown, but fixed, probability distribution on the training data.

Original languageEnglish (US)
Title of host publication1997 IEEE International Conference on Neural Networks, ICNN 1997
Pages1068-1071
Number of pages4
DOIs
StatePublished - 1997
Externally publishedYes
Event1997 IEEE International Conference on Neural Networks, ICNN 1997 - Houston, TX, United States
Duration: Jun 9 1997Jun 12 1997

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume2
ISSN (Print)1098-7576

Conference

Conference1997 IEEE International Conference on Neural Networks, ICNN 1997
Country/TerritoryUnited States
CityHouston, TX
Period6/9/976/12/97

ASJC Scopus subject areas

  • Software

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