Title

Experiments With Safe Μartmap And Comparisons To Other Art Networks

Abstract

Fuzzy ARTMAP (FAM) is currently considered as one of the premier neural network architectures in solving classification problems. Safe μARTMAP, a modified version of FAM, was introduced to remedy the category proliferation problem that has been extensively reported in the literature. However, Safe μARTMAP's performance depends on a number of parameters. In this paper, we analyzed each parameter to set up the candidate values for evaluation. We performed an exhaustive experimentation to identify good default values for these parameters for a variety of problems, and compared the best performing Safe μARTMAP network with other best performing ART networks, including those that claim to solve the category proliferation problem. ©2006 IEEE.

Publication Date

12-1-2006

Publication Title

IEEE International Conference on Neural Networks - Conference Proceedings

Number of Pages

720-727

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

40649106770 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/40649106770

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