Performance of back propagation networks for associative database retrieval

dc.contributor.author	Cherkassky, Vladimir	en
dc.contributor.author	Βασιλάς, Νικόλαος	el
dc.date.accessioned	2015-05-12T19:44:09Z
dc.date.issued	2015-05-12
dc.identifier.uri	http://hdl.handle.net/11400/10257
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://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=118562&abstractAccess=no&userType=inst	en
dc.subject	δίκτυα διάδοσης
dc.subject	συνδυαστική ανάκτηση της βάσης δεδομένων
dc.subject	τοπολογία δικτύου
dc.subject	propagation networks
dc.subject	associative database retrieval
dc.subject	Electric network topology
dc.title	Performance of back propagation networks for associative database retrieval	en
heal.type	conferenceItem
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/sh85041865
heal.identifier.secondary	DOI: 10.1109/IJCNN.1989.118562
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	1989-06
heal.bibliographicCitation	Cherkassky, V. and Vassilas, N. (1989) Performance of back propagation networks for associative database retrieval. Proc. IEEE IJCNN. 1, pp.77- 84. Washington: IEEE	en
heal.abstract	Back-propagation networks have been successfully used to perform a variety of input-output mapping tasks for recognition, generalization, and classification. In spite of this method's popularity, virtually nothing is known about its saturation/capacity and, in more general terms, about its performance as an associative memory. The authors address these issues using associative database retrieval as an original application domain. Experimental results show that the quality of recall and the network capacity are very significantly affected by the network topology (the number of hidden units), data representation (encoding), and the choice of learning parameters. On the basis of their results and the fact that back-propagation learning is not recursive, the authors conclude that back-propagation networks can be used mainly as read-only associative memories and represent a poor choice for read-and-write associative memories.	en
heal.publisher	IEEE	en
heal.fullTextAvailability	false
heal.conferenceName	Proc. IEEE IJCNN	en
heal.conferenceItemType	other