Title

A Partitioned Fuzzy Artmap Implementation For Fast Processing Of Large Databases On Sequential Machines

Abstract

Fuzzy ARTMAP (FAM) is a neural network architecture that can establish the correct mapping between real valued input patterns and their correct labels. FAM can learn quickly compared to other neural network paradigms and has the advantage of incremental/online learning capabilities. Nevertheless FAM tends to slow down as the size of the data set grows. This problem is analyzed and a solution is proposed that can speed up the algorithm in sequential as well as parallel settings. Experimental results are presented that show a considerable improvement in speed of the algorithm at the cost of creating larger size FAM architectures. Directions for future work are also discussed.

Publication Date

12-17-2004

Publication Title

Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2004

Volume

2

Number of Pages

623-628

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

10044259899 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/10044259899

This document is currently not available here.

Share

COinS