Symbolic Classifier with Convex Hull Based Dissimilarity Function

  • Francisco A.T. de Carvalho
  • Simith T. D’Oliveira Júnior
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


This work presents a new symbolic classifier based on a region oriented approach. At the end of the learning step, each class is described by a region (or a set of regions) in ℜp defined by the convex hull of the objects belonging to this class. In the allocation step, the assignment of a new object to a class is based on a dissimilarity matching function that compares the class description (a region or a set of regions) with a point in ℜp. This approach aims to reduce the over-generalization that is produced when each class is described by a region (or a set of regions) defined by the hyper-cube formed by the objects belonging to this class. It then seeks to improve the classifier performance. In order to show its usefulness, this approach was applied to a study of simulated SAR images.


Synthetic Aperture Radar Classifier Performance Maximal Clique Class Description Speckle Noise 


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Copyright information

© Springer-Verlag Berlin · Heidelberg 2005

Authors and Affiliations

  • Francisco A.T. de Carvalho
    • 1
  • Simith T. D’Oliveira Júnior
    • 1
  1. 1.Centro de Informática - UFPECidade UniversitáriaRecife - PEBrasil

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