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dc.contributor.authorElizondo, Daviden
dc.contributor.authorTajine, M.en
dc.date.accessioned2008-11-24T13:24:10Z
dc.date.available2008-11-24T13:24:10Z
dc.date.issued2002-08-01en
dc.identifier.citationTajine, M., and Elizondo, D. (2002) New methods for testing linear separability. Neurocomputing, 47(1-4), pp. 161-188.
dc.identifier.issn0925-2312en
dc.identifier.urihttp://hdl.handle.net/2086/172
dc.descriptionThis paper introduces latest advances in the subject of linear separability. New methods for testing linear separability are introduced. This is a very important area of work which can help simplify the topology of a neural network by using a single layer perceptron when the problem at hand is linearly separable. The research presented in this paper has allowed researchers to enhance the performance of the RDP neural network. It appears in one of the leading journals of Neural Networks.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectRAE 2008
dc.subjectUoA 23 Computer Science and Informatics
dc.subjectlinear separability
dc.subjectconvex hull
dc.subjectperceptron
dc.subjectclass of separability
dc.titleNew methods for testing linear separability.en
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1016/S0925-2312(01)00587-2en
dc.researchgroupDIGITSen
dc.researchgroupCentre for Computational Intelligence
dc.peerreviewedYesen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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