Title
AutoAssociative pyramidal neural network for one class pattern classification with implicit feature extraction
Author
Faculty/Department
Faculty of Sciences. Physics
Publication type
article
Publication
New York ,
Subject
Economics
Mathematics
Computer. Automation
Source (journal)
Expert systems with applications. - New York
Volume/pages
40(2013) :18 , p. 7258-7266
ISSN
0957-4174
ISI
000324663000011
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Receptive fields and autoassociative memory are brain concepts that have individually inspired many artificial models, but models using both ideas have not been deeply studied. In this paper, we propose the AutoAssociative Pyramidal Neural Network (AAPNet), which is an artificial neural network for one-class classification that uses autoassociative memory and receptive field concepts in its pyramidal architecture. The proposed neural network performs implicit feature extraction and learns how to reconstruct a pattern from such features. The AAPNet is evaluated using the object categorization Caltech-101 database and presents better results when compared with other state-of-the-art methods. (C) 2013 Elsevier Ltd. All rights reserved.
E-info
https://repository.uantwerpen.be/docman/iruaauth/ca79bf/7bd6309.pdf
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