Tools
ToolsGabrys, 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:
![[img]](/style/images/fileicons/application_pdf.png)
|
PDF
Gabrys_NNSP2000.pdf - Accepted Version 977kB | |
|
Copyright to original material in this document is with the original owner(s). Access to this content through BURO is granted on condition that you use it only for research, scholarly or other non-commercial purposes. If you wish to use it for any other purposes, you must contact BU via BURO@bournemouth.ac.uk. Any third party copyright material in this document remains the property of its respective owner(s). BU grants no licence for further use of that third party material. |
Official URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arn...
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) |
|---|---|
| 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 |
Downloads
Downloads per month over past year
| Repository Staff Only - |