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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 2/2010

01.03.2010 | KES 2008

A novel Euclidean quality threshold ARTMAP network and its application to pattern classification

verfasst von: Shahrul Nizam Yaakob, C. P. Lim, Lakhmi Jain

Erschienen in: Neural Computing and Applications | Ausgabe 2/2010

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Abstract

This paper introduces a novel neural network model known as the Euclidean quality threshold ARTMAP (EQTAM) network and its application to pattern classification. The model is constructed based on fuzzy ARTMAP (FAM) and the quality threshold clustering principle. The main objective of EQTAM is to overcome the effects of training data sequences on FAM and, at the same time, to improve its classification performance. Several artificial data sets and benchmark medical data sets are used to evaluate the effectiveness of the proposed model. Performance comparisons between EQTAM and ARTMAP-based as well as other classifiers are made. From the experimental results, it can be observed that EQTAM is able to produce good results. More importantly, the performance of EQTAM is robust against the effect of training data orders or sequences.
Vorheriger Artikel Accelerated gradient learning algorithm for neural network weights update
Nächster Artikel Application of neural processing paradigm in visual landmark recognition and autonomous robot navigation

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Metadaten
Titel
A novel Euclidean quality threshold ARTMAP network and its application to pattern classification
verfasst von
Shahrul Nizam Yaakob
C. P. Lim
Lakhmi Jain
Publikationsdatum
01.03.2010
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 2/2010
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-009-0293-8

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