Multiple Classifier Systems for Embedded String Patterns

  • Barbara Spillmann
  • Michel Neuhaus
  • Horst Bunke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4087)


Multiple classifier systems are a well proven and tested instrument for enhancing the recognition accuracy in statistical pattern recognition problems. However, there has been reported only little work on combining classifiers in structural pattern recognition. In this paper we describe a method for embedding strings into real vector spaces based on prototype selection, in order to gain several vectorial descriptions of the string data. We present methods for combining multiple classifiers trained on various vectorial data representations. As base classifiers we use nearest neighbor methods and support vector machine. In our experiments we demonstrate that this approach can be used to significantly improve the classification accuracy of string patterns.


Support Vector Machine Radial Basis Function Recognition Rate Edit Distance Edit Operation 
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

  • Barbara Spillmann
    • 1
  • Michel Neuhaus
    • 1
  • Horst Bunke
    • 1
  1. 1.Institute of Computer Science and Applied MathematicsUniversity of BernBernSwitzerland

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