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dc.contributor.authorElizondo, Daviden
dc.contributor.authorBirkenhead, Ralph, 1955-en
dc.contributor.authorGongora, Mario Augustoen
dc.contributor.authorLuyima, P.en
dc.contributor.authorTaillard, Eric
dc.date.accessioned2008-11-24T13:24:11Z
dc.date.available2008-11-24T13:24:11Z
dc.date.issued2007-12-01en
dc.identifier.citationElizondo, D.A. et al. (2007) Analysis and test of efficient methods for building recursive deterministic perceptron neural networks. Neural Networks, 20 (10), pp. 1095-1108.
dc.identifier.issn0893-6080en
dc.identifier.urihttp://hdl.handle.net/2086/175
dc.descriptionThis paper introduces a comparison study of three existing methods for building Recursive Deterministic Perceptron Neural Networks. Three methods were compared in terms of their level of generalisation, convergence time and topology sizes. Prior to this study only an exhaustive, NP-Complete method was used for building RDP neural networks. Due to its high complexity, this limited its use in real world classification problems. This work shows that the other two methods, with a polynomial time complexity, can be used as an alternative. These results will widen the use of the RDP neural network. The impact factor is 2.000.en
dc.language.isoenen
dc.publisherNeural Networksen
dc.subjectRAE 2008
dc.subjectUoA 23 Computer Science and Informatics
dc.subjectrecursive deterministic perceptron
dc.subjectbatch learning
dc.subjectincremental learning
dc.subjectmodular learning
dc.subjectperformance sensitivity analysis
dc.subjectconvergence time
dc.subjecttopology
dc.titleAnalysis and test of efficient methods for building recursive deterministic perceptron neural networks.en
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1016/J.neunet.2007.07.009en
dc.researchgroupDIGITSen
dc.researchgroupCentre for Computational Intelligence
dc.peerreviewedYesen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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