NDLI: Agglomerative learning for general fuzzy min-max neural network

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Agglomerative learning for general fuzzy min-max neural network

Content Provider	IEEE Xplore Digital Library
Author	Gabrys, B.
Copyright Year	2000
Description	Author affiliation: Appl. Comput. Intelligence Res. Unit, Univ. of Paisley, UK (Gabrys, B.)
Abstract	Proposes an agglomerative learning algorithm based on similarity measures defined for hyperbox fuzzy sets. It is presented in a context of clustering and classification problems that are tackled using a general fuzzy min-max (GFMM) neural network. The agglomerative scheme's robust behaviour in the presence of noise and outliers and its insensitivity to the order of the training pattern presentation are used as a complementary features to an incremental learning scheme, making it more suitable for online adaptation and dealing with large training data sets.
Starting Page	692
Ending Page	701
File Size	537706
Page Count	10
File Format	PDF
ISBN	0780362780
ISSN	10893555
DOI	10.1109/NNSP.2000.890148
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-12-11
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks Neural networks Fuzzy sets Clustering algorithms Adaptive systems Machine learning algorithms Artificial neural networks Machine learning Fuzzy systems Computational intelligence
Content Type	Text
Resource Type	Article

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