Exemplar-based pattern recognition via semi-supervised learning
Georgios C. Anagnostopoulos, M. Bharadwaj, Michael Georgiopoulos, Stephen Verzi, Gregory L. Heileman · 2004
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.