A constraint inductive learning-spectral clustering methodology for personalized 3D navigation

dc.contributor.author	Δουλάμης, Αναστάσιος	el
dc.contributor.author	Γιακουμέττης, Χ.	el
dc.contributor.author	Πρωτοπαπαδάκης, Ευτύχιος	el
dc.contributor.author	Μιαούλης, Γεώργιος	el
dc.date.accessioned	2015-06-05T16:31:01Z
dc.date.available	2015-06-05T16:31:01Z
dc.date.issued	2015-06-05
dc.identifier.uri	http://hdl.handle.net/11400/15152
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://link.springer.com	en
dc.subject	Inductive learning
dc.subject	Ηλεκτρονική μάθηση
dc.subject	Επαγωγική μάθηση
dc.subject	Online learning
dc.subject	Φασματική ομαδοποίηση
dc.subject	Spectral clustering
dc.title	A constraint inductive learning-spectral clustering methodology for personalized 3D navigation	en
heal.type	journalArticle
heal.classification	Technology
heal.classification	Computer programming ( URL: http://skos.um.es/unescothes/C00749)
heal.classification	Τεχνολογία
heal.classification	Προγραμματισμός
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	N/A-Computer programming ( URL: http://skos.um.es/unescothes/C00749)
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Προγραμματισμός
heal.identifier.secondary	DOI: 10.1007/978-3-642-41939-3_11
heal.language	en
heal.access	campus
heal.publicationDate	2013
heal.bibliographicCitation	Doulamis, N., Yiakoumettis, C., Miaoulis, G. and Protopapadakis, E. (2013) A constraint inductive learning-spectral clustering methodology for personalized 3D navigation. "Lecture Notes in Computer Science", 8034 (2), p.108-117	en
heal.abstract	The recent advances in ICT boost research towards the generation of personalized Geographical Information Systems (p-GIS). It is clear that selection of a route based only on geometrical criteria, i.e., the route of the shortest distance or the minimum travel time, very rarely coincides with a "satisfactory itinerary" that respects users' preferences, that is their desires to navigate through buildings or places of his/her own particular interest. Additionally, 3D navigation gains more popularity compared with 2D approaches especially in virtual tourist and cultural heritage applications. In a p-GIS, user's preferences can be set manually or automatically. In an automatic architecture, user preferences are expressed as a set of weights that regulate the degree of importance on the route selection process and on line learning strategies are exploited to adjust the weights. In this paper, the on-line learning strategy exploits information fed back to the system about the relevance of user's preferences judgments given in a form of pair-wise comparisons. Then, we use a constraint fusion methodology for the dynamic modeling of user's preference in a 3D navigation system. The method exploits an active inductive learning approach that is combined with an adaptive spectral clustering scheme in order to avoid smoothing during the weight adjustment process.	en
heal.publisher	Springer	en
heal.journalName	Lecture Notes in Computer Science	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true