Abstract
Principles of separable aggregation of multichannel (multisource) data sets by parallel layers of formal neurons are considered in the paper. Each data set contains such feature vectors which represent objects assigned to one of a few categories.The term multichannel data sets means that each single object is characterised by data obtained through different information channels and represented by feature vectors in a different feature space. Feature vectors from particular feature spaces are transformed by layers of formal neurons what results in the aggregation of some feature vectors. The postulate of separable aggregation is aimed at the minimization of the number of different feature vectors under the condition of preserving the categories separabilty.
Keywords
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.
This work was partially supported by the W/II/1/2006 and SPUB-M (COST 282) grants from the Białystok University of Technology and by the 16/St/2006 grant from the Institute of Biocybernetics and Biomedical Engineering PAS.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Rosenblatt, F.: Principles of neurodynamics. Spartan Books, Washington (1962)
Duda, O.R., Hart, P.E., Stork, D.G.: Pattern Classification. J. Wiley, New York (2000)
Bobrowski, L.: Piecewise-Linear Classifiers, Formal Neurons and Separability of the Learning Sets. In: Proceedings of ICPR 1996, Wienna, Austria, pp. 224–228 (1996)
Bobrowski, L.: Eksploracja danych oparta na wypuk’ych i odcinkowo-liniowych funkcjach kryterialnych (Data mining based on convex and piecewise linear (CPL) criterion functions ) (in Polish), Technical University Białystok (2005)
Bobrowski, L.: Design of piecewise linear classifiers from formal neurons by some basis exchange technique. Pattern Recognition 24(9), 863–870 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bobrowski, L. (2006). Multichannel Data Aggregation by Layers of Formal Neurons. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_1
Download citation
DOI: https://doi.org/10.1007/11785231_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35748-3
Online ISBN: 978-3-540-35750-6
eBook Packages: Computer ScienceComputer Science (R0)