ARTMAP Module Documentation


These pages describe the implementation of the ARTMAP algorithm (see articles here) used in Classer.

The code implements the Distributed ARTMAP model, but with the appropriate RunModeType setting, it also implements the Fuzzy ARTMAP, default ARTMAP and instance-counting ARTMAP models. More specifically, it unifies the algorithms published in:

Default ARTMAPCarpenter, G.A. (2003). Default ARTMAP. Proceedings of the International Joint Conference on Neural Networks (IJCNN'03), Portland, Oregon.
Distributed ARTMAPCarpenter, G.A., Milenova, B., & Noeske, B. (1998). dARTMAP: A neural network for fast distributed supervised learning. Neural Networks, 11, 793-813

ARTMAP Notation.png

ARTMAP Notation

The four available ARTMAP models differ in whether their category representation during training and testing is winner-take-all (wta) or distributed, as well as in whether or not categories are weighted by the number of training samples they encode (instance counting). These differences are summarized by the following table:

TrainingTestingInstance Counting

Based on which of the four models is chosen, different paths are taken through the implementation and different predictive results are obtained when evaluating data sets. Flowcharts documenting the implementations are shown in the documentation for the train() and test() methods of the artmap class.

Licensing Policy

The ARTMAP implementation described here has been developed by Siegfried Martens, working in the CELEST Technology Laboratory, in the Department of Cognitive and Neural Systems at Boston University, Boston, Massachusetts, USA.

It is made available to the general public under the terms of Copyleft, as defined by the Free Software Foundation (see discussion here for example). As such, it is available free of charge, and may be copied, redistributed, or modified, as long as any resulting work is distributed under the same terms of Copyleft.
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