Abstract
Neural networks, Bayesian networks, Markov models, and state predictors are different methods to predict the next location. For all methods a lot of parameters must be set up which differ for each user. Therefore a complex configuration must be made before such a method can be used. A hybrid predictor can reduce the configuration overhead utilizing different prediction methods or configurations in parallel to yield different prediction results. A selector chooses the most appropriate prediction result from the result set of the base predictors. We propose and evaluate three principal hybrid predictor approaches – the warm-up predictor, the majority predictor, and the confidence predictor – with several variants. The hybrid predictors reached a higher prediction accuracy than the average of the prediction accuracies of the separately used predictors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Petzold, J., Bagci, F., Trumler, W., Ungerer, T.: Global and Local Context Prediction. In: Artificial Intelligence in Mobile Systems 2003 (AIMS 2003), Seattle, WA, USA (2003)
Vintan, L., Gellert, A., Petzold, J., Ungerer, T.: Person Movement Prediction Using Neural Networks. In: First Workshop on Modeling and Retrieval of Context, Ulm, Germany (2004)
Petzold, J., Pietzowski, A., Bagci, F., Trumler, W., Ungerer, T.: Prediction of Indoor Movements Using Bayesian Networks. In: Strang, T., Linnhoff-Popien, C. (eds.) LoCA 2005. LNCS, vol. 3479, Springer, Heidelberg (2005)
Chen, I.C.K., Coffey, J.T., Mudge, T.N.: Analysis of Branch Prediction via Data Compression. In: ASPLOS VII, Cambridge, Massachusetts, USA, pp. 128–137 (1996)
Ross, S.M.: Introduction to Probability Models. Academic Press, London (1985)
Petzold, J., Bagci, F., Trumler, W., Ungerer, T.: Confidence Estimation of the State Predictor Method. In: 2nd European Symposium on Ambient Intelligence, Eindhoven, The Netherlands, pp. 375–386 (2004)
Petzold, J.: Augsburg Indoor Location Tracking Benchmarks. Context Database, Institute of Pervasive Computing, University of Linz, Austria (2005), http://www.soft.uni-linz.ac.at/Research/Context_Database/index.php
Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing 7, 275–286 (2003)
Bhattacharya, A., Das, S.K.: LeZi-Update: An Information-Theoretic Framework for Personal Mobility Tracking in PCS Networks. Wireless Networks 8, 121–135 (2002)
Kaowthumrong, K., Lebsack, J., Han, R.: Automated Selection of the Active Device in Interactive Multi-Device Smart Spaces. In: Borriello, G., Holmquist, L.E. (eds.) UbiComp 2002. LNCS, vol. 2498, Springer, Heidelberg (2002)
Mozer, M.C.: The Neural Network House: An Environment that Adapts to its Inhabitants. In: AAAI Spring Symposium on Intelligent Environments, Menlo Park, CA, USA, pp. 110–114 (1998)
Patterson, D.J., Liao, L., Fox, D., Kautz, H.: Inferring High-Level Behavior from Low-Level Sensors. In: 5th International Conference on Ubiquitous Computing, Seattle, WA, USA, pp. 73–89 (2003)
Young, C., Smith, M.D.: Improving the Accuracy of Static Branch Prediction Using Branch Correlation. In: Proceedings of the Sixth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS-VI), San Jose, USA, pp. 232–241 (1994)
Grunwald, D., Klauser, A., Manne, S., Pleszkun, A.: Confidence Estimation for Speculation Control. In: Proceedings of the 29th Annual International Symposium on Computer Architecture, Barcelona, Spain, pp. 122–131 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Petzold, J., Bagci, F., Trumler, W., Ungerer, T. (2006). Hybrid Predictors for Next Location Prediction. In: Ma, J., Jin, H., Yang, L.T., Tsai, J.JP. (eds) Ubiquitous Intelligence and Computing. UIC 2006. Lecture Notes in Computer Science, vol 4159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11833529_13
Download citation
DOI: https://doi.org/10.1007/11833529_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38091-7
Online ISBN: 978-3-540-38092-4
eBook Packages: Computer ScienceComputer Science (R0)