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.
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.
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© 2006 Springer-Verlag Berlin Heidelberg
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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
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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
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