Internal categories with irregular geometry and overlapping in ART networks

Authors:
Dinani Gomes;Manuel Fernández-Delgado;Senén Barro
Affiliations:
Intelligent Systems Group, Dept. of Electronics and Computer Science, University of Santiago de Compostela, Santiago de Compostela, A Coruña, Spain;Intelligent Systems Group, Dept. of Electronics and Computer Science, University of Santiago de Compostela, Santiago de Compostela, A Coruña, Spain;Intelligent Systems Group, Dept. of Electronics and Computer Science, University of Santiago de Compostela, Santiago de Compostela, A Coruña, Spain
Venue:
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
Year:
2005

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Abstract

PolyTope ARTMAP (PTAM) [6] is an ART neural network based on internal categories with irregular polytope (polygon in IRn) geometry. Categories in PTAM do not overlap, so that their expansion is limited by the other categories, and not by the category size. This makes the vigilance parameter unnecessary. What happens if categories have irregular geometries but overlap is allowed? This paper presents Overlapping PTAM (OPTAM), an alternative to PTAM based on polytope overlapping categories, which tries to answer this question. The comparison between the two approaches in classification tasks shows that category overlap does not reduce neither the classification error nor the number of categories, and it also requires vigilance as a tuning parameter. Futhermore, OPTAM provides a significant variability in the results among different data sets.