@inproceedings{41f5efa1e51e4c09adf524e37c88e23a,
title = "A procedure to select the vigilance threshold for the ART2 for supervised and unsupervised training",
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.",
author = "{Ray{\'o}n Villela}, P. and {Sossa Azuela}, {J. H.}",
year = "2000",
doi = "10.1007/10720076_35",
language = "Ingl{\'e}s",
isbn = "3540673547",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "389--400",
booktitle = "MICAI 2000",
note = "1st Mexican International Conference on Artificial Intelligence, MICAI 2000 ; Conference date: 11-04-2000 Through 14-04-2000",
}