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Robust Neural Network for Novelty Detection on Data Streams

  • Andrzej Rusiecki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7267)

Abstract

In the on-line data processing it is important to detect a novelty as soon as it appears, because it may be a consequence of gross errors or sudden change in the analysed system. In this paper we present a framework of novelty detection, based on the robust neural network. To detect novel patterns we compare responses of two autoregressive neural networks. One of them is trained with a robust learning algorithm designed to remove the influence of outliers, while the other uses simple training, based on the least squares error criterion. We present also a simple and easy to use approach that adapts this technique to data streams. Experiments conducted on data containing novelty and outliers have shown promising performance of the new method, applied to analyse temporal sequences.

Keywords

Data Stream Outlier Detection Feedforward Neural Network Network Output Training Pattern 
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 2012

Authors and Affiliations

  • Andrzej Rusiecki
    • 1
  1. 1.Wroclaw University of TechnologyWroclawPoland

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