Publication
Title
AutoAssociative pyramidal neural network for one class pattern classification with implicit feature extraction
Author
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.
Language
English
Source (journal)
Expert systems with applications. - New York
Publication
New York : 2013
ISSN
0957-4174
Volume/pages
40:18(2013), p. 7258-7266
ISI
000324663000011
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 16.12.2013
Last edited 16.11.2017
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