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Kalman Filters and Adaptive Windows for Learning in Data Streams

  • Albert Bifet
  • Ricard Gavaldà
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4265)

Abstract

We study the combination of Kalman filter and a recently proposed algorithm for dynamically maintaining a sliding window, for learning from streams of examples. We integrate this idea into two well-known learning algorithms, the Naïve Bayes algorithm and the k-means clusterer. We show on synthetic data that the new algorithms do never worse, and in some cases much better, than the algorithms using only memoryless Kalman filters or sliding windows with no filtering.

Keywords

Kalman Filter Data Stream Synthetic Data Linear Estimator Concept Drift 
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

  • Albert Bifet
    • 1
  • Ricard Gavaldà
    • 1
  1. 1.Universitat Politècnica de CatalunyaBarcelonaSpain

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