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Hybrid Predictors for Next Location Prediction

  • Jan Petzold
  • Faruk Bagci
  • Wolfgang Trumler
  • Theo Ungerer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4159)

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.

Keywords

Prediction Accuracy Bayesian Network Threshold Method Simple Majority Location Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jan Petzold
    • 1
  • Faruk Bagci
    • 1
  • Wolfgang Trumler
    • 1
  • Theo Ungerer
    • 1
  1. 1.Institute of Computer ScienceUniversity of AugsburgGermany

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