Efficient neural network-based methodology for the design of multiple classifiers

dc.contributor.author	Βασιλάς, Νικόλαος	el
dc.date.accessioned	2015-05-11T22:59:12Z
dc.date.available	2015-05-11T22:59:12Z
dc.date.issued	2015-05-12
dc.identifier.uri	http://hdl.handle.net/11400/10180
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.researchgate.net/publication/268424273_EFFICIENT_NEURAL_NETWORK-BASED_METHODOLOGY_FOR_THE_DESIGN_OF_MULTIPLE_CLASSIFIERS	en
dc.subject	Μεθοδολογία
dc.subject	νευρωνικό δίκτυο
dc.subject	Πολλαπλοί ταξινομητές
dc.subject	αποδοτική μνήμη
dc.subject	ταξινόμηση
dc.subject	αλγόριθμος
dc.subject	Research--Methodology
dc.subject	Neural networks
dc.subject	Multiple Classifiers
dc.subject	memory efficient
dc.subject	classification
dc.subject	algorithm
dc.title	Efficient neural network-based methodology for the design of multiple classifiers	en
heal.type	bookChapter
heal.generalDescription	σε έντυπη μορφή στο γραφείο μου	el
heal.classification	Πληροφορική
heal.classification	Μηχανική υπολογιστών
heal.classification	Computer science
heal.classification	Computer engineering
heal.classificationURI	N/A-Πληροφορική
heal.classificationURI	N/A-Μηχανική υπολογιστών
heal.classificationURI	http://skos.um.es/unescothes/C00750
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85029495
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2002009792
heal.keywordURI	http://zbw.eu/stw/descriptor/19808-6
heal.identifier.secondary	ISBN: 9781439821992
heal.language	en
heal.access	campus
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	2000-03-09
heal.bibliographicCitation	Vassilas, N. (2000) Efficient Neural Network-Based Methodology for the Design of Multiple Classifiers. In: Jain, L.C. and Fanelli, A.M. (eds.). Recent Advances in Artificial Neural Networks: Design and Applications. New York: CRC Press	en
heal.abstract	A neural network-based methodology for time and memory efficient supervised or unsupervised classification in heavily demanding applications is presented in this chapter. Significantly increased speed in the design (training) of neural, fuzzy and statistical classifiers as well as in the classification phase is achieved by: (a) using a self-organizing feature map (SOFM) for vector quantization and indexed representation of the input data space; (b) appropriate training set reduction using the SOFM prototypes followed by necessary modifications of the training algorithms (supervised techniques); (c) clustering of neurons on maps instead of clustering the original data (unsupervised techniques); and (d) fast indexed classification. Finally, a demonstration of this method-ology involving the design of multiple classifiers is performed on Land-Cover classification of multispectral satellite image data showing increased speed with respect to both training and classification times.	en
heal.publisher	CRC Press	en
heal.fullTextAvailability	true
heal.bookName	Recent Advances in Artificial Neural Networks	en