Separable Data Aggregation by Layers of Binary Classifiers
Leon Bobrowski , Magdalena Topczewska
AbstractAggregating layers can be designed from binary classifiers on the principle of preserving data sets separability. Formal neurons or logical elements are treated here as basic examples of binary classifiers. Learning data sets are composed of such feature vectors which are linked to particular categories (classes). Separability of the learning sets is preserved during transformation of feature vectors from these sets by a dipolar layer of binary classifiers. The dipolar layer separates all such pairs of feature vectors that have been linked to different classes and belong to different learning sets.
|Publication size in sheets||0.55|
|Book||Nguyen Ngoc Thanh, Gaol Ford Lumban, Hong Tzung-Pei, Trawiński Bogdan (eds.): Intelligent Information and Database Systems, Lecture Notes In Computer Science, vol. 11431, 2019, Springer, ISBN 978-3-030-14798-3, 754 p.|
|Keywords in English||Feature vectors, Binary classifiers, Separable data aggregation, Dipolar aggregation, Hierarchical networks, Deep learning|
|Internal identifier||ROC 19-20|
|Score||= 20.0, 28-01-2020, ChapterFromConference|
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