Agglomerative Learning for General Fuzzy Min-Max Neural Network.

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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, pp. 692-701.

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Official URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arn...

DOI: 10.1109/NNSP.2000.890148

Abstract

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)
Subjects:Generalities > Computer Science and Informatics > Artificial Intelligence
Generalities > Computer Science and Informatics
Group:School of Design, Engineering & Computing > Smart Technology Research Centre
ID Code:9646
Deposited By:Professor Bogdan Gabrys
Deposited On:11 Mar 2009 22:45
Last Modified:07 Mar 2013 15:07

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