Title

An ordering algorithm for pattern presentation in fuzzy ARTMAP that tends to improve generalization performance

Authors

Authors

I. Dagher; M. Georgiopoulos; G. L. Heileman;G. Bebis

Comments

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Abbreviated Journal Title

IEEE Trans. Neural Netw.

Keywords

fuzzy ARTMAP; generalization; learning; max-min clustering; CLASSIFICATION; ARCHITECTURE; Computer Science, Artificial Intelligence; Computer Science, Hardware &; Architecture; Computer Science, Theory & Methods; Engineering, ; Electrical & Electronic

Abstract

In this paper we introduce a procedure, based on the max-min clustering method, that identifies a fixed order of training pattern presentation for fuzzy adaptive resonance theory mapping (ARTMAP). This procedure is referred to as the ordering algorithm, and the combination of this procedure with fuzzy ARTMAP is referred to as ordered fuzzy ARTMAP. Experimental results demonstrate that ordered fuzzy ARTMAP exhibits a generalization performance that is better than the average generalization performance of fuzzy ARTMAP, and: in certain cases as good as, or better than the best fuzzy ARTMAP generalization performance. We abo:calculate the number of operations required by the ordering algorithm and compare it to the number of operations required by the training phase of fuzzy ARTMAP, We show that, under mild assumptions, the number of operations required by the ordering algorithm is a fraction of the number of operations required by fuzzy ARTMAP.

Journal Title

Ieee Transactions on Neural Networks

Volume

10

Issue/Number

4

Publication Date

1-1-1999

Document Type

Article

Language

English

First Page

768

Last Page

778

WOS Identifier

WOS:000081385700004

ISSN

1045-9227

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