Quality threshold ARTMAP neural network for pattern classification and prediction /
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(Published version)
Date
2010
Authors
Yaakob, Shahrul Nizam,
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Journal Title
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Type:
thesis
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Abstract
This thesis presents a novel technique based on the combination of fundamental concept of Fuzzy ARTMAP (FAM) neural network and Quality Threshold clustering technique. The proposed network called Quality Threshold ARTMAP (QTAM) is implemented for pattern classification and recognition task. Several benchmark data sets had been adopted in order to test the efficiency of the network for pattern classification and recognition task. This work also presents our investigation of QTAM network in two different real world applications namely face recognition and insect classification. In face recognition problem, three different types of feature extraction technique such as Principal Component Analysis (PCA), Modified PCA and Bidirectional 2-Dimensional PCA are used to generate the feature vectors for face images.
School/Discipline
University of South Australia. School of Electrical and Information Engineering.
School of Electrical and Information Engineering
School of Electrical and Information Engineering
Dissertation Note
Thesis (PhDComputerSystemEng)--University of South Australia, 2010.
Provenance
Copyright 2010 Shahrul Nizam Yaakob. This work is made available under the Creative Commons Attribution-NonCommercial-NoDerivs Australia 3.0 licence (http://creativecommons.org/licenses/by-nc-nd/3.0/au/)
Description
xix, 279 leaves :
illustrations.
Includes bibliographic references.
illustrations.
Includes bibliographic references.
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506 0#$fstar $2Unrestricted online access