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Agglomerative Learning for General Fuzzy Min-Max Neural Network.

Gabrys, B., 2000. Agglomerative Learning for General Fuzzy Min-Max Neural Network. In: IEEE International Workshop on Neural Networks for Signal Processing, 11-13 December 2000, Sydney, Australia, 692-701.

Full text available as:

Gabrys_NNSP2000.pdf - Accepted Version


Official URL:

DOI: 10.1109/NNSP.2000.890148


In this paper an agglomerative learning algorithm based on similarity measures defined for hyperbox fuzzy sets is proposed. 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

Item Type:Conference or Workshop Item (Paper)
Group:Faculty of Science & Technology
ID Code:9646
Deposited By: Professor Bogdan Gabrys LEFT
Deposited On:11 Mar 2009 22:45
Last Modified:14 Mar 2022 13:21


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