Advertisement

Separable Data Aggregation by Layers of Binary Classifiers

  • Leon BobrowskiEmail author
  • Magdalena Topczewska
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11431)

Abstract

Aggregating layers can be designed from binary classifiers on the principle of preserving data sets separability. Formal neurons or logical elements are treated here as basic examples of binary classifiers. Learning data sets are composed of such feature vectors which are linked to particular categories (classes). Separability of the learning sets is preserved during transformation of feature vectors from these sets by a dipolar layer of binary classifiers. The dipolar layer separates all such pairs of feature vectors that have been linked to different classes and belong to different learning sets.

Keywords

Feature vectors Binary classifiers Separable data aggregation Dipolar aggregation Hierarchical networks Deep learning 

Notes

Acknowledgments

The presented study was supported by the grant S/WI/2/2013 from Bialystok University of Technology and funded from the resources for research by Polish Ministry of Science and Higher Education.

References

  1. 1.
    Hand, D., Smyth, P., Mannila, H.: Principles of Data Mining. MIT Press, Cambridge (2001)Google Scholar
  2. 2.
    Duda, O.R., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Hoboken (2001)zbMATHGoogle Scholar
  3. 3.
    Bobrowski, L.: Data mining based on convex and piecewise linear criterion functions. Technical University Białystok (2005). (in Polish)Google Scholar
  4. 4.
    Bobrowski, L.: Piecewise-linear classifiers, formal neurons and separability of the learning sets. In: Proceedings of ICPR 1996, pp. 224–228, 13th International Conference on Pattern Recognition, Vienna, Austria, 25–29 August 1996 (1996)Google Scholar
  5. 5.
    Bobrowski, L., Topczewska, M.: Dipolar data aggregation in the context of deep learning. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 574–583. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01424-7_56CrossRefGoogle Scholar
  6. 6.
    Bobrowski, L., Topczewska, M.: Linearizing layers of radial binary classifiers with movable centers. Pattern Anal. Appl. 18(4), 771–781 (2015)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Arel, I., Rose, D.C., Karnowski, T.P.: Deep machine learning – a new frontier in artificial intelligence – a survey paper. IEEE Comput. Intell. Mag. (2013)Google Scholar
  8. 8.
    Little, M.A., McSharry, P.E., Roberts, S.J., Costello, D.A.E., Moroz, I.M.: Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. BioMed. Eng. OnLine 6, 23 (2007)CrossRefGoogle Scholar
  9. 9.
    Wang, Z., et al.: Sparse Coding and its Applications in Computer Vision. World Scientific, Hackensack (2016)zbMATHGoogle Scholar
  10. 10.
    Dua, D., Karra Taniskidou, E.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2017). http://archive.ics.uci.edu/ml

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Computer ScienceBialystok University of TechnologyBialystokPoland
  2. 2.Institute of Biocybernetics and Biomedical Engineering, PASWarsawPoland

Personalised recommendations