Abstract
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.
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© 2000 Springer-Verlag Berlin Heidelberg
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Rayón Villela, P., Sossa Azuela, J.H. (2000). A Procedure to Select the Vigilance Threshold for the ART2 for Supervised and Unsupervised Training. In: Cairó, O., Sucar, L.E., Cantu, F.J. (eds) MICAI 2000: Advances in Artificial Intelligence. MICAI 2000. Lecture Notes in Computer Science(), vol 1793. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10720076_35
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DOI: https://doi.org/10.1007/10720076_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67354-5
Online ISBN: 978-3-540-45562-2
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