| Bibliography | 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.
University of Stuttgart : Collaborative Research Center SFB 627 (Nexus: World Models for Mobile Context-Based Systems).
pp. 197-200, english.
New York, NY, USA: ACM, November 2009.
ISBN: 978-1-60558-710-3.
Article in Proceedings (Conference Paper).
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| CR-Schema | 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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| Abstract | 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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| Department(s) | University of Stuttgart, Institute of Parallel and Distributed Systems, Image Understanding
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| Project(s) | SFB-627, C3 (University of Stuttgart, Institute of Parallel and Distributed Systems, Image Understanding)
SFB-627, E3 (University of Stuttgart, Institute of Parallel and Distributed Systems, Image Understanding)
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| Entry date | May 27, 2010 |
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