Experiments with Safe ARTMAP and Comparisons to Other ART Networks. Zhong, M.; Rosander, B.; Georgiopoulos, M.; Anagnostopoulos, G. C.; Mollaghasemi, M.; and Richie, S. In Neural Networks, 2006. IJCNN '06. International Joint Conference on, pages 720-727, 2006.
doi  abstract   bibtex   
Fuzzy ARTMAP (FAM) is currently considered as one of the premier neural network architectures in solving classification problems. Safe muARTMAP, a modified version of FAM, was introduced to remedy the category proliferation problem that has been extensively reported in the literature. However, Safe muARTMAP's performance depends on a number of parameters. In this paper, we analyzed each parameter to set up the candidate values for evaluation. We performed an exhaustive experimentation to identify good default values for these parameters for a variety of problems, and compared the best performing Safe muARTMAP network with other best performing ART networks, including those that claim to solve the category proliferation problem.
@InProceedings{Zhong2006a,
  author    = {Mingyu Zhong and Rosander, B. and Georgiopoulos, Michael and Anagnostopoulos, Georgios C. and Mollaghasemi, Mansooreh and Richie, S.},
  title     = {Experiments with Safe ARTMAP and Comparisons to Other ART Networks},
  booktitle = {Neural Networks, 2006. IJCNN '06. International Joint Conference on},
  year      = {2006},
  pages     = {720-727},
  abstract  = {Fuzzy ARTMAP (FAM) is currently considered as one of the premier neural
	network architectures in solving classification problems. Safe muARTMAP,
	a modified version of FAM, was introduced to remedy the category
	proliferation problem that has been extensively reported in the literature.
	However, Safe muARTMAP's performance depends on a number of parameters.
	In this paper, we analyzed each parameter to set up the candidate
	values for evaluation. We performed an exhaustive experimentation
	to identify good default values for these parameters for a variety
	of problems, and compared the best performing Safe muARTMAP network
	with other best performing ART networks, including those that claim
	to solve the category proliferation problem.},
  doi       = {10.1109/IJCNN.2006.246755},
  keywords  = {ART neural nets;fuzzy neural nets;neural net architecture;pattern classification;ART networks;category proliferation problem;classification problem solving;fuzzy ARTMAP;neural network architectures;safe muARTMAP;Computer science;Fuzzy logic;Fuzzy sets;Fuzzy systems;Helium;Industrial engineering;Neural networks;Pattern recognition;Resonance;Subspace constraints},
}
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