Induction of Linear Separability through the Ranked Layers of Binary Classifiers
The concept of linear separability is used in the theory of neural networks and pattern recognition methods. This term can be related to examination of learning sets (classes) separation by hyperplanes in a given feature space. The family of K disjoined learning sets can be transformed into K linearly separable sets by the ranked layer of binary classifiers. Problems of the ranked layers deigning are analyzed in the paper.
KeywordsLearning sets linear separability formal neurons binary classifiers ranked
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