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Induction of Linear Separability through the Ranked Layers of Binary Classifiers

Abstract : 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.
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Leon Bobrowski. Induction of Linear Separability through the Ranked Layers of Binary Classifiers. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. pp.69-77, ⟨10.1007/978-3-642-23957-1_8⟩. ⟨hal-01571330⟩

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