Publication Type: Book Chapter
Source: Lecture Notes in Artificial Intelligence, Springer, Volume 4177, Santiago de Compostela (ESPAÑA), p.291-300 (2006)
PolyTope ARTMAP (PTAM)  is an ART neural network based on internal categories with irregular polytope (polygon in R^n ) 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 overlapping 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 overlapping 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.