The concept of linear separability is used in the theory of neural networks and pattern recognition methods. This term can be related to examination of learning sets (classes) separation by hyperplanes in a given feature space. The family of K disjoined learning sets can be transformed into K linearly separable sets by the ranked layer of binary classifiers. Problems of the ranked layers deigning are analyzed in the paper.


Learning sets linear separability formal neurons binary classifiers ranked 


  1. 1.
    Rosenblatt, F.: Principles of neurodynamics. Spartan Books, Washington (1962)MATHGoogle Scholar
  2. 2.
    Minsky, M.L., Papert, S.A.: Perceptrons. MIT Press, Cambridge (1969)MATHGoogle Scholar
  3. 3.
    Duda, O.R., Hart, P.E., Stork, D.G.: Pattern classification. J. Wiley, New York (2001)MATHGoogle Scholar
  4. 4.
    Bobrowski, L.: Eksploracja danych oparta na wypukłych i odcinkowo-liniowych funkcjach kryterialnych (Data mining based on convex and piecewise linear (CPL) criterion functions), Technical University Białystok (2005) (in Polish) Google Scholar
  5. 5.
    Bobrowski, L., Łukaszuk, T.: Feature selection based on relaxed linear separability. Biocybernetics and Biomedcal Engineering 29(2), 43–59 (2009)Google Scholar
  6. 6.
    Vapnik, V.N.: Statistical Learning Theory. J. Wiley, New York (1998)MATHGoogle Scholar
  7. 7.
    Bobrowski, L.: Design of piecewise linear classifiers from formal neurons by some basis exchange technique. Pattern Recognition 24(9), 863–870 (1991)CrossRefGoogle Scholar

Copyright information

© International Federation for Information Processing 2011

Authors and Affiliations

  • Leon Bobrowski
    • 1
    • 2
  1. 1.Faculty of Computer ScienceBialystok Technical UniversityBialystok
  2. 2.Institute of Biocybernetics and Biomedical Engineering, PASWarsawPoland

Personalised recommendations