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Author(s): Amis, G.P. | Carpenter, G.A. |
Year: 2007
Citation: Proceedings of the International Joint Conference on Neural Networks (IJCNN 07 @ Orlando).
Abstract: Default ARTMAP combines winner-take-all category node activation during training, distributed activation during testing, and a set of default parameter values that define a ready-to-use, general-purpose neural network system for supervised learning and recognition. Winner-take-all ARTMAP learning is designed so that each input would make a correct prediction if re-presented immediately after its training presentation, passing the ?next-input test.? Distributed activation has been shown to improve test set prediction on many examples, but an input that made a correct winner-take-all prediction during training could make a different prediction with distributed activation. Default ARTMAP 2 introduces a distributed next-input test during training. On a number of benchmarks, this additional feature of the default system increases accuracy without significantly decreasing code compression. This paper includes a self-contained default ARTMAP 2 algorithm for implementation.
Topics:
Machine Learning,
Models:
ARTMAP,
Modified ART,
