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Information Analysis of Multiple Classifier Fusion?

  • Jiří Grim
  • Josef Kittler
  • Pavel Pudil
  • Petr Somol
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2096)

Abstract

We consider a general scheme of parallel classifier combinations in the framework of statistical pattern recognition. Each statistical classifier defines a set of output variables in terms of a posteriori probabilities, i.e. it is used as a feature extractor. Unlike usual combining schemes the output vectors of classifiers are combined in parallel. The statistical Shannon information is used as a criterion to compare different combining schemes from the point of view of the theoretically available decision information. By means of relatively simple arguments we derive a theoretical hierarchy between different schemes of classifier fusion in terms of information inequalities.

Keywords

Information Loss Information Analysis Probabilistic Neural Network Posteriori Probability Statistical Pattern Recognition 
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 2001

Authors and Affiliations

  • Jiří Grim
    • 1
  • Josef Kittler
    • 2
  • Pavel Pudil
    • 1
  • Petr Somol
    • 1
  1. 1.Institute of Information Theory and AutomationPrague 8Czech Republic
  2. 2.School of Electronic Engineering, Information Technology and MathematicsUniversity of SurreyGuildfordUK

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