Theoretical analysis of the batch variant of the self-organizing feature map algorithm for 1-d networks mapping a continuous 1-d input space

dc.contributor.author	Βασιλάς, Νικόλαος	el
dc.date.accessioned	2015-05-11T19:26:04Z
dc.date.available	2015-05-11T19:26:04Z
dc.date.issued	2015-05-11
dc.identifier.uri	http://hdl.handle.net/11400/10165
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.tandfonline.com/doi/abs/10.1080/00207169808804653	en
dc.subject	αυτο-οργανωμένοι χάρτες
dc.subject	διαδικασίες Μάρκοβ
dc.subject	αλγόριθμος
dc.subject	Self-organizing maps
dc.subject	Markov processes
dc.subject	Algorithm
dc.title	Theoretical analysis of the batch variant of the self-organizing feature map algorithm for 1-d networks mapping a continuous 1-d input space	en
heal.type	journalArticle
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/sh99004370
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85081369
heal.identifier.secondary	DOI: 10.1080/00207169808804653
heal.language	en
heal.access	campus
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	1998
heal.bibliographicCitation	Vassilas, N. (1998) Theoretical analysis of the batch variant of the self-organizing feature map algorithm for 1-d networks mapping a continuous 1-d input space. International Journal of Computer Mathematics. [Online] 67 (1-2), pp.77-103. Available from: http://www.tandfonline.com [Accessed 11/05/2015]	en
heal.abstract	This work investigates the batch variant of Kohonen's self-organizing feature map (SOFM) algorithm both analytically and with simulations. In this algorithm, the winning neurons as well as the weight updates are computed in batch mode (epoch mode). It is shown that for 1-D maps and 1-D continuous input and weight spaces the strictly increasing or decreasing weight configurations form absorbing classes provided certain conditions on the parameters are satisfied. Ordering of the maps, convergence in distribution and asymptotic convergence are also proved analytically. Finally, simulations and comparisons with the original Kohonen algorithm on 1-D and 2-D maps are provided and are found to be in complete agreement with the theoretical results.	en
heal.publisher	Taylor & Francis	en
heal.journalName	International Journal of Computer Mathematics	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true