Batch self-organizing map algorithm

dc.contributor.author	Βασιλάς, Νικόλαος	el
dc.date.accessioned	2015-05-15T14:16:43Z
dc.date.issued	2015-05-15
dc.identifier.uri	http://hdl.handle.net/11400/10447
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=4634061&abstractAccess=no&userType=inst	en
dc.subject	αυτο-οργανούμενοι χάρτες
dc.subject	διαδικασίες Μάρκοβ
dc.subject	νευρωνικά δίκτυα
dc.subject	επιδράσεις της κβάντωσης
dc.subject	Self-organizing maps
dc.subject	Neural networks
dc.subject	quantization effects
dc.title	Batch self-organizing map algorithm	en
heal.type	conferenceItem
heal.secondaryTitle	a theoretical study of self-organization of a 1- D network under quantization effects	en
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/sh99004370
heal.keywordURI	http://zbw.eu/stw/descriptor/19808-6
heal.identifier.secondary	DOI: 10.1109/IJCNN.2008.4634061
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	2008-06-01
heal.bibliographicCitation	Vassilas, N. (2008) Batch self-organizing map algorithm: A theoretical study of self-organization of a 1-D network under quantization effects. Proceedings IEEE International Joint Conference on Neural Networks (IJCNN'08), WCCI 2008. pp.1925-1930. Hong-Kong: IEEE.	en
heal.abstract	In this paper, we examine necessary and sufficient conditions that ensure self-organization of the batch variant of the self-organizing map algorithm for 1-D networks and for quantized weights and inputs. Using Markov chain formalism, it is shown that the existing analysis for the original algorithm can be extended to also include the more general batch variant. Finally, simulations verify the theoretical results, relate the speed of weight ordering to the distribution of the inputs and show the existence of metastable states of the Markov chain.	en
heal.publisher	IEEE	en
heal.fullTextAvailability	false
heal.conferenceName	IEEE International Joint Conference on Neural Networks (IJCNN'08), WCCI 2008	en
heal.conferenceItemType	short paper