28 June 1994 Adaptive pattern recognition by self-organizing neural networks
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Proceedings Volume 10312, Neural and Fuzzy Systems: The Emerging Science of Intelligent Computing; 1031209 (1994) https://doi.org/10.1117/12.2283792
Event: SPIE Institutes for Advanced Optical Technologies 12, 1994, Bellingham, WA, United States
Abstract
Nonlinear decision boundaries separating similar regions in unlabeled data sets are encountered in many real applications but are extremely difficult to generate by statistical decision models. Supervised multilayered neural networks are capable of generating some nonlinear decision boundaries but require extensive training procedures leading to computational burden as well. Unsupervised self-organizing neural nets are capable of forming crisp clusters from unlabeled data sets. A recent trend has been to integrate the concept of fuzzy sets with adaptive learning inherent to neural nets. Such integration yields not only better clustering of similar data groups, it may also provide a method for generating nonlinear boundaries among clusters in close proximity. The weaknesses and strengths of such integrated self-organizing neuro-fuzzy models for adaptive pattern recognition are described. Successful classification of standard data sets and generation of nonlinear decision boundaries among neighboring clusters from computer generated data are demonstrated with a recently developed integrated adaptive fuzzy clustering (IAFC) model.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sunanda Mitra, Sunanda Mitra, } "Adaptive pattern recognition by self-organizing neural networks", Proc. SPIE 10312, Neural and Fuzzy Systems: The Emerging Science of Intelligent Computing, 1031209 (28 June 1994); doi: 10.1117/12.2283792; https://doi.org/10.1117/12.2283792
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