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Evaluation of the fuzzy ARTMAP neural network using off-line and on-line learning strategies

journal contribution
posted on 1999-01-01, 00:00 authored by Chee Peng LimChee Peng Lim, H Toh, T Lee
This paper describes an experimental study of the Fuzzy ARTMAP (FAM) neural network as an autonomous learning system for pattern classification tasks. A benchmark database of radar signals from ionosphere has been employed for the system to classify arbitrary sequences of pattern into distinct categories. A number of simulations have been conducted systematically to evaluate the applicability and usefulness of FAM in this context. First, we identify the 'optimum' parameter settings of FAM for the problem at hand, and investigate the effects of different training schemes and learning rules on classification results, using an off-line learning methodology. We then examine a voting strategy to improve classification accuracy by combining results from multiple FAM classifiers. In addition to off-line learning, we evaluate the prospect of using FAM as an autonomously learning pattern classification system for on-line, non-stationary environments. The performance of FAM is comparable with other reported results, but with the added advantage of on-line and incremental learning.

History

Journal

Neural network world

Volume

9

Issue

4

Pagination

327 - 339

Publisher

Akademie Ved Ceske Republiky

Location

Prague, Czech Republic

ISSN

1210-0552

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

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