The linear separability problem: some testing methods

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dc.contributor.author Elizondo, David en
dc.date.accessioned 2008-11-24T13:24:14Z
dc.date.available 2008-11-24T13:24:14Z
dc.date.issued 2006-03-01 en
dc.identifier.citation Elizondo, D.A. (2006) The linear separability problem: some testing methods. IEEE Transactions on Neural Networks, 17(2), pp. 330-344.
dc.identifier.issn 1045-9227 en
dc.identifier.uri http://hdl.handle.net/2086/185
dc.description This article presents an analysis of some of the methods for testing linear separability. A single layer perceptron neural network can be used for creating a classification model when the classes at hand are linearly separable. Since the RDP neural network is based on linearly separable subsets within a non linearly separable set, the performance of the method used for searching these subsets is of great importance in order to minimise convergence time, and maximise the level of generalisation. It appears in one of the leading journals of Neural Networks with an impact factor of 2.620. en
dc.language.iso en en
dc.publisher IEEE en
dc.subject RAE 2008
dc.subject UoA 23 Computer Science and Informatics
dc.subject class of separability
dc.subject computational geometry
dc.subject Fisher linear discriminant
dc.subject linear programming
dc.title The linear separability problem: some testing methods en
dc.type Article en
dc.identifier.doi http://dx.doi.org/10.1109/TNN.2005.860871 en
dc.researchgroup Centre for Computational Intelligence
dc.researchgroup DIGITS en
dc.peerreviewed Yes


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