Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/450
Title: A comparative study of the category choice of the fuzzy ART with the L -1 norm
Authors: Dagher, Issam 
Affiliations: Department of Computer Engineering 
Keywords: Fuzzy neural nets
Category theory
ART neural nets
Learning (artificial intelligence)
Issue Date: 2003
Publisher: IEEE
Part of: Proceedings of the International Joint Conference on Neural Networks, 2003
Start page: 1969
End page: 1974
Conference: International Joint Conference on Neural Networks (20-24 July 2003 : Portland, OR, USA) 
Abstract: 
In this paper, a comparative study of the category choice of the Fuzzy ART with the L-1 norm is presented. It is shown that the category choice can be replaced by a distance measure related to the L-1 norm. This distance measure will have the following advantages over the category choice of the Fuzzy ART network: 1) no need for augmenting the dimensions of the input patterns. The distance measure will operate directly on the input patterns without the need for doing complement coding; and 2) no need for normalizing the input patterns. The input patterns need not to be in the interval.
URI: https://scholarhub.balamand.edu.lb/handle/uob/450
Ezproxy URL: Link to full text
Type: Conference Paper
Appears in Collections:Department of Computer Engineering

Show full item record

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.