title: Polytope ARTMAP: pattern classification without vigilance based on general geometry categories.
abstract: This paper proposes polytope ARTMAP (PTAM), an adaptive resonance theory (ART) network for classification tasks which does not use the vigilance parameter. This feature is due to the geometry of categories in PTAM, which are irregular polytopes whose borders approximate the borders among the output predictions. During training, the categories expand only towards the input pattern without category overlap. The category expansion in PTAM is naturally limited by the other categories, and not by the category size, so the vigilance is not necessary. PTAM works in a fully automatic way for pattern classification tasks, without any parameter tuning, so it is easier to employ for nonexpert users than other classifiers. PTAM achieves lower error than the leading ART networks on a complete collection of benchmark data sets, except for noisy data, without any parameter optimization.
authors.affiliation: Department of Electronics and Computer Science, University of Santiago de Compostela, Santiago de Compostela 15706, Spain. dinani@usc.es
authors.forename: Dinani
authors.initials: D
authors.lastname: Gomes Amorim
authors.forename: Manuel
authors.initials: M
authors.lastname: Fernández Delgado
authors.forename: Senén
authors.initials: S
authors.lastname: Barro Ameneiro
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity