The Adaptive Resonance Theory ART2 [1] is used as a non supervised tool to generate clusters. The clusters generated by an ART2 Neural Network (ART2 NN), depend on a vigilance threshold (ρ). If ρ is near to zero, then a lot of clusters will be generated; if ρ is greater then more clusters will be generated. To get a good performance, this ρ has to be suitable selected for each problem. Until now, no technique had been proposed to automatically select a proper ρ for a specific problem. In this paper we present a first way to automatically obtain the value of ρ, we also illustrate how it can be used in supervised and unsupervised learning. The goal to select a suitable threshold is to reach a better performance at the moment of classification. To improve classification, we also propose to use a set of feature vectors instead of only one to describe the objects. We present some results in the case of character recognition. © Springer-Verlag Berlin Heidelberg 2000.
CITATION STYLE
Rayón Villela, P., & Sossa Azuela, J. H. (2000). A procedure to select the vigilance threshold for the ART2 for supervised and unsupervised training. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1793 LNAI, pp. 389–400). https://doi.org/10.1007/10720076_35
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