Induction of Linear Separability through the Ranked Layers of Binary Classifiers

  • Leon Bobrowski
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 363)

Abstract

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.

Keywords

Learning sets linear separability formal neurons binary classifiers ranked 

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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

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