Dwulit’s Hull as Means of Optimization of kNN Algorithm

  • M. P. Dwulit
  • Z. Szymański
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 99)


The paper includes a description of a novel method for reducing the size of a training set in order to reduce memory requirements and classification complexity. Our method allows the condensing of the training set in a way that it is both training set consistent (classifies all training data points correctly) and decision-boundary consistent (the decision boundary does not changes after applying our method) for NN classifiers. Furthermore, the algorithm described here allows the utilization of a parallel computing paradigm in order to increase performance.


Convex Hull Voronoi Diagram Near Neighbor Delaunay Triangulation Decision Boundary 
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

  • M. P. Dwulit
    • 1
  • Z. Szymański
    • 1
  1. 1.Department of Computer ScienceWarsaw University of TechnologyWarsawPoland

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