Bibliograph.
Daten | Zweigle, Oliver; Häussermann, Kai; Käppeler, Uwe-Philipp; Levi, Paul: Supervised learning algorithm for automatic adaption of situation templates using uncertain data.
In: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human.
Universität Stuttgart : Sonderforschungsbereich SFB 627 (Nexus: Umgebungsmodelle für mobile kontextbezogene Systeme).
S. 197-200, englisch.
New York, NY, USA: ACM, November 2009.
ISBN: 978-1-60558-710-3.
Artikel in Tagungsband (Konferenz-Beitrag).
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| CR-Klassif. | I.2.3 (Deduction and Theorem Proving)
I.2.4 (Knowledge Representation Formalisms and Methods)
I.2.5 (Artificial Intelligence Programming Languages and Software)
I.2.6 (Artificial Intelligence Learning)
I.2.8 (Problem Solving, Control Methods, and Search)
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| Kurzfassung | In this paper a learning algorithm for the automatic adaption of a situation template is presented. The approach strongly relies on human-machine interaction as user feedback is a substantial part to automatically adapt a global knowledgebase in this case. The work bases on the assumption of uncertain data and includes elements from the domain of Bayesian Networks and Machine Learning. It is embedded into the cluster of excellence Nexus at the University of Stuttgart which has the aim to build a distributed context aware user-friendly system for sharing context data.
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| Abteilung(en) | Universität Stuttgart, Institut für Parallele und Verteilte Systeme, Bildverstehen
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| Projekt(e) | SFB-627, C3 (Universität Stuttgart, Institut für Parallele und Verteilte Systeme, Bildverstehen)
SFB-627, E3 (Universität Stuttgart, Institut für Parallele und Verteilte Systeme, Bildverstehen)
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| Eingabedatum | 27. Mai 2010 |
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