Linear algebra approach to neural associative memories and noise performance of neural classifiers

dc.contributor.author	Cherkassky, Vladimir	en
dc.contributor.author	Βασιλάς, Νικόλαος	el
dc.contributor.author	Fassett, K.	en
dc.date.accessioned	2015-05-10T21:53:36Z
dc.date.issued	2015-05-11
dc.identifier.uri	http://hdl.handle.net/11400/10102
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.computer.org/csdl/trans/tc/1991/12/t1429-abs.html	en
dc.subject	Γραμμική Άλγεβρα
dc.subject	Νευρωνικές συνειρμικές μνήμες
dc.subject	Απόδοση θορύβου
dc.subject	Νευρωνικοί ταξινομητές
dc.subject	Linear algebra
dc.subject	Neural Associative Memories
dc.subject	Noise Performance
dc.subject	Neural Classifiers
dc.title	Linear algebra approach to neural associative memories and noise performance of neural classifiers	en
heal.type	journalArticle
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://skos.um.es/unesco6/120110
heal.identifier.secondary	DOI: 10.1109/12.106229
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	1991-12
heal.bibliographicCitation	Cherkassky, V., Vassilas, N. and Fassett, K. (1991) Linear algebra approach to neural associative memories and noise performance of neural classifiers. IEEE Transactions on Computers. [Online] 40 (12), pp.1429-1435. Available from: http://www.computer.org [Accessed 10/05/2015]	en
heal.abstract	The authors present an analytic evaluation of saturation and noise performance for a large class of associative memories based on matrix operations. The importance of using standard linear algebra techniques for evaluating noise performance of associative memories is emphasized. The authors present a detailed comparative analysis of the correlation matrix memory and the generalized inverse memory construction rules for auto-associative memory and neural classifiers. Analytic results for the noise performance of neural classifiers that can store several prototypes in one class are presented. The analysis indicates that for neural classifiers the simple correlation matrix memory provides better noise performance than the more complex generalized inverse memory.	en
heal.journalName	IEEE Transactions on Computers	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false