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dc.contributor.authorElizondo, Daviden
dc.date.accessioned2008-11-24T13:24:14Z
dc.date.available2008-11-24T13:24:14Z
dc.date.issued2006-03-01en
dc.identifier.citationElizondo, D.A. (2006) The linear separability problem: some testing methods. IEEE Transactions on Neural Networks, 17(2), pp. 330-344.
dc.identifier.issn1045-9227en
dc.identifier.urihttp://hdl.handle.net/2086/185
dc.descriptionThis 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.isoenen
dc.publisherIEEEen
dc.subjectRAE 2008
dc.subjectUoA 23 Computer Science and Informatics
dc.subjectclass of separability
dc.subjectcomputational geometry
dc.subjectFisher linear discriminant
dc.subjectlinear programming
dc.titleThe linear separability problem: some testing methodsen
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1109/TNN.2005.860871en
dc.researchgroupCentre for Computational Intelligence
dc.researchgroupDIGITSen
dc.peerreviewedYesen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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