Separable data aggregation in hierarchical networks of formal neurons

Authors:
Leon Bobrowski
Affiliations:
Faculty of Computer Science, Bialystok Technical University, Institute of Biocybernetics and Biomedical Engineering, PAS, Warsaw, Poland
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

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ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
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Abstract

In this paper we consider principles of such data aggregation in hierarchical networks of formal neurons which allows one to preserve the separability of the categories. The postulate of the categories separation in the layers of formal neurons is examined by means of the concept of clear and mixed dipoles. Dependence of separation of the categories on the feature selection is analysed.