Temporal Data Classification Using Linear Classifiers

  • Peter Revesz
  • Thomas Triplet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5739)


Data classification is usually based on measurements recorded at the same time. This paper considers temporal data classification where the input is a temporal database that describes measurements over a period of time in history while the predicted class is expected to occur in the future. We describe a new temporal classification method that improves the accuracy of standard classification methods. The benefits of the method are tested on weather forecasting using the meteorological database from the Texas Commission on Environmental Quality.


Support Vector Machine Feature Space Probabilistic Induction Move Object Database Wind Speed Component 
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 2009

Authors and Affiliations

  • Peter Revesz
    • 1
  • Thomas Triplet
    • 1
  1. 1.University of Nebraska - LincolnLincolnUSA

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