Supervised Pattern Recognition with Heterogeneous Features

  • Ventzeslav Valev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)


In this paper, we address the supervised pattern recognition problem with heterogeneous features, where the mathematical model is based on construction of thresholds. Non-Reducible Descriptors (NRDs) for fuzzy features are obtained through the use of a threshold value, which is calculated based on the distance between patterns. For solving the problem with real features the mathematical model for construction of thresholds is based on parallel feature partitioning. Boolean formulas are used to represent NRDs.


Dissimilarity Matrix Boolean Formula Real Feature Binary Feature Heterogeneous Feature 
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 2004

Authors and Affiliations

  • Ventzeslav Valev
    • 1
  1. 1.College of Art and Sciences, Department of Mathematics and Computer ScienceSaint Louis UniversitySt. LouisUSA

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