Exemplar-based Pattern Recognition via Semi-Supervised Learning

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

Research output: Contribution to conferencePaperpeer-review

21 Scopus citations


The focus of this paper is semi-supervised learning in the context of pattern recognition. Semi-supervised learning (SSL) refers to the semi-supervised construction of clusters during the training phase of exemplar-based classifiers. Using artificially generated data sets we present experimental results of classifiers that follow the SSL paradigm and we show that, especially for difficult pattern recognition problems featuring high class overlap, for exemplar-based classifiers implementing SSL i) the generalization performance improves, while ii) the number of necessary exemplars decreases significantly, when compared to the original versions of the classifiers.

Original languageEnglish (US)
Number of pages6
StatePublished - 2003
Externally publishedYes
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: Jul 20 2003Jul 24 2003


ConferenceInternational Joint Conference on Neural Networks 2003
Country/TerritoryUnited States
CityPortland, OR

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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