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Proceedings Paper

Adaptive pattern recognition by self-organizing neural networks
Author(s): Sunanda Mitra
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Paper 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.

Paper Details

Date Published: 28 June 1994
PDF: 30 pages
Proc. SPIE 10312, Neural and Fuzzy Systems: The Emerging Science of Intelligent Computing, 1031209 (28 June 1994); doi: 10.1117/12.2283792
Show Author Affiliations
Sunanda Mitra, Texas Tech Univ. (United States)

Published in SPIE Proceedings Vol. 10312:
Neural and Fuzzy Systems: The Emerging Science of Intelligent Computing
Sunanda D. Mitra; Madan M. Gupta; Wolfgang F. Kraske, Editor(s)

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