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. © Springer-Verlag Berlin Heidelberg 2005.
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CITATION STYLE
Bobrowski, L. (2005). Separable data aggregation in hierarchical networks of formal neurons. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3697 LNCS, pp. 289–294). https://doi.org/10.1007/11550907_46
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