Gabrys, B., 2002. Agglomerative learning algorithms for general fuzzy min-max neural network. Journal of VLSI Signal Processing Systems, 32 (1-2), pp. 67-82.
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Official URL: http://www.springerlink.com/content/u8tt321h848770...
In this paper two agglomerative learning algorithms based on new similarity measures defined for hyperbox fuzzy sets are proposed. They are presented in a context of clustering and classification problems tackled using a general fuzzy min-max (GFMM) neural network. The proposed agglomerative schemes have shown robust behaviour in presence of noise and outliers and insensitivity to the order of training patterns presentation. The emphasis is also put on the complimentary features to the previously presented incremental learning scheme more suitable for on-line adaptation and dealing with large training data sets. The performance and other properties of the agglomerative schemes are illustrated using a number of artificial and real-world data sets.
|Additional Information:||This is an invited, extended version of the paper selected from the IEEE Conference on Neural Networks for Signal Processing (NNSP'2000). This paper is another example from the series of papers related to the GFMM algorithms which provides an alternative and complementary agglomerative learning algorithms to the previously proposed on-line learning algorithms published in the IEEE Transactions on Neural Networks in 2000 which have been widely referenced. Within the paper new similarity measures for hyperbox fuzzy sets are proposed which can lead to asymmetrical similarity matrices which in themselves are quite interesting and uncommon. The properties of 2 different novel agglomerative learning algorithms are extensively illustrated, tested and analysed.|
|Uncontrolled Keywords:||pattern classification, hierarchical clustering, agglomerative learning, neuro-fuzzy system, hyperbox fuzzy sets|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
Generalities > Computer Science and Informatics
|Group:||School of Design, Engineering & Computing > Smart Technology Research Centre|
|Deposited By:||INVALID USER|
|Deposited On:||17 Dec 2007|
|Last Modified:||07 Mar 2013 14:35|
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