Separable Data Aggregation by Layers of Binary Classifiers

Leon Bobrowski , Magdalena Topczewska

Abstract

Aggregating 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.
Author Leon Bobrowski (FCS / SD)
Leon Bobrowski,,
- Software Department
, Magdalena Topczewska (FCS / SD)
Magdalena Topczewska,,
- Software Department
Pages327-338
Publication size in sheets0.55
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 EnglishFeature vectors, Binary classifiers, Separable data aggregation, Dipolar aggregation, Hierarchical networks, Deep learning
Internal identifierROC 19-20
Languageen angielski
Score (nominal)20
Score sourceconferenceList
ScoreMinisterial score = 20.0, 28-01-2020, ChapterFromConference
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