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.
Full text available as:
|PDF - Accepted Version|
Official URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arn...
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:||Faculty of Science and Technology|
|Deposited By:||Professor Bogdan Gabrys|
|Deposited On:||11 Mar 2009 22:45|
|Last Modified:||10 Sep 2014 15:44|
Document DownloadsMore statistics for this item...
|Repository Staff Only -|
|BU Staff Only -|
|Help Guide -||Editing Your Items in BURO|