Abstract
A layer of formal neurons can perform separable data aggregation. The term (separable data aggregation( means that a number of input vectors belonging to one category (class) are merged by the layer in one output vector with an additional condition that input vectors belonging to different categories are not aggregated. Dipolar principles of separable layers designing are examined in the paper. Hierarchical networks can be designed from separable layers and used for aggregation of all input vectors belonging to one category in an output vector.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rosenblatt, F.: Principles of neurodynamics. Spartan Books, Washington (1962)
Minsky, M.L., Papert, S.A.: Perceptrons. MIT Press, Cambridge (1969)
Duda, O.R., Hart, P.E., Stork, D.G.: Pattern classification. J. Wiley, New York (2001)
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)
Bobrowski, L.: Piecewise-Linear Classifiers, Formal Neurons and Separability of the Learning Sets. In: Proceedings of ICPR 1996, Vienna, pp. 224–228 (1996)
Bobrowski, L.: Design of piecewise linear classifiers from formal neurons by some basis exchange technique. Pattern Recognition 24(9), 863–870 (1991)
Bobrowski, L.: Induction of Linear Separability through the Ranked Layers of Binary Classifiers. In: Iliadis, L., Jayne, C. (eds.) EANN/AIAI 2011, Part I. IFIP AICT, vol. 363, pp. 69–77. Springer, Heidelberg (2011)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bobrowski, L. (2012). Dipolar Designing Layers of Formal Neurons. In: Jayne, C., Yue, S., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2012. Communications in Computer and Information Science, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32909-8_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-32909-8_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32908-1
Online ISBN: 978-3-642-32909-8
eBook Packages: Computer ScienceComputer Science (R0)