Machine learning and pattern analysis methods for profiling in a declarative collaorative framework

dc.contributor.author	Δουλάμης, Νικόλαος Δ.	el
dc.contributor.author	Δραγονάς, Ιωάννης	el
dc.contributor.author	Δουλάμης, Αναστάσιος Δ.	el
dc.contributor.author	Μιαούλης, Γεώργιος	el
dc.contributor.author	Πλεμενός, Δημήτρης	el
dc.date.accessioned	2015-06-07T07:46:46Z
dc.date.available	2015-06-07T07:46:46Z
dc.date.issued	2015-06-07
dc.identifier.uri	http://hdl.handle.net/11400/15394
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/bookseries/7092	en
dc.subject	Declarative Modeling
dc.subject	Independent/ dependent user' profile modeling
dc.subject	Performance--Evaluation
dc.subject	User's preferences estimation
dc.subject	Δηλωτική Μοντελοποίηση
dc.subject	Ανεξάρτητη / εξαρτημένη μοντελοποίηση προφίλ χρήστη
dc.subject	Αξιολόγηση Απόδοσης
dc.subject	Εκτίμηση προτιμήσεων χρήστη
dc.title	Machine learning and pattern analysis methods for profiling in a declarative collaorative framework	en
heal.type	conferenceItem
heal.classification	Computer science
heal.classification	Artificial intelligence
heal.classification	Πληροφορική
heal.classification	Τεχνητή νοημοσύνη
heal.classificationURI	http://skos.um.es/unescothes/C00750
heal.classificationURI	http://skos.um.es/unescothes/C00261
heal.classificationURI	N/A-Πληροφορική
heal.classificationURI	N/A-Τεχνητή νοημοσύνη
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2010105499
heal.identifier.secondary	DOI: 10.1007/978-3-642-03452-7_10
heal.language	en
heal.access	campus
heal.recordProvider	Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	2009
heal.bibliographicCitation	Doulamis, N., Dragonas, J., Doulamis, A., Miaoulis, G. and Plemenos, D. (2009) Machine learning and pattern analysis methods for profiling in a declarative collaorative framework. In "Intelligent Computer Graphics 2009". [n.p.]: [n.n.]. Available from: http://link.springer.com/chapter/10.1007%2F978-3-642-03452-7_10 [Accessed: 07/06/2015].	en
heal.abstract	One important issue for a collaborative design framework is the intuitive manner designers describe scenes. To address the humans’ subjective description of a scene, personalization mechanisms are incorporated in the proposed architecture. In this paper, we examine different pattern analysis and machine intelligence algorithms for profiling in such framework. Two main approaches are described; the single (independent) profile estimation and the collaborative (dependent) case. Experimental results are presented to illustrate the efficiency of the proposal user’ profile methodologies.	en
heal.fullTextAvailability	true
heal.conferenceName	Intelligent Computer Graphics 2009	en
heal.conferenceItemType	full paper