ARTMAP-IC and medical diagnosis: Instance counting and inconsistent cases

Data-partitioning using the Hilbert space filling curves: Effect on the speed of convergence of Fuzzy ARTMAP for large database problems

“…Since its introduction, a number of Fuzzy ARTMAP variations and associated successful applications of this ART family of neural networks have appeared in the literature (for instance, ARTEMAP (Carpenter & Ross, 1995), ARTMAP-IC (Carpenter & Markuzon, 1998), Ellisoid-ART/ARTMAP ), Fuzzy Min-Max (Simpson, 1992), LAPART2 (Caudell & Healy, 1999), and σ-FLNMAP (Petridis, Kaburlasos, Fragkou, & Kehagais, 2001), to mention only a few. For the purposes of the discussion that follows in this section it is worth mentioning again the work by Kasuba (Kasuba, 1993) and Taghi, Baghmisheh, and Pavesic (Taghi et al, 2003).…”

ARTMAP neural networks for information fusion and data mining: map production and target recognition methodologies

“…For distributed coding, the transformation of the filtered bottom-up input to an activation pattern across a field of nodes is defined by the increased-gradient CAM rule (Carpenter, Milenova, & Noeske, 1998). The network also implements the MT-search algorithm (Carpenter & Markuzon, 1998), with the baseline vigilance parameter set equal to zero, for maximal code compression. Other ARTMAP design choices for default ARTMAP include fast learning, whereby weights converge to asymptote on each learning trial; single-epoch training, which emulates on-line learning; and a choice-by-difference signal function (Carpenter & Gjaja, 1994) from the input field to the coding field.…”

“…Meanwhile, new ARTMAP systems that have been developed over the past decade include ART-EMAP (Carpenter & Ross, 1995), ARTMAP-IC (Carpenter & Markuzon, 1998), and distributed ARTMAP (Carpenter, Milenova, & Noeske, 1998). Network capabilities and design options have been tested, and system performance has been compared with that of other neural and statistical algorithms, on many application domains, including remote sensing, data mining, and visualization (e.g., Carpenter et al, 1997Carpenter et al, , 1999Gopal, Liu, & Woodcock, 2000;Gopal, Woodcock, & Strahler, 1999).…”

A What-and-Where fusion neural network for recognition and tracking of multiple radar emitters

“…Fuzzy ARTMAP and three other ARTMAP neural networks-.. ART-EMAP (Stage 1) (Carpenter and Ross, 1995), ARTMAP-IC (Carpenter and Markuzon, 1998) and Gaussian ARTMAP (Williamson, 1996(Williamson, , 1997) -have been compared using computer simulations. The kNN and RBF classifiers were included for non-parametric, and semi-parametric (Bishop, 1995) reference, respectively.…”