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dc.contributor.authorCalcraft, L.
dc.contributor.authorAdams, Roderick
dc.contributor.authorDavey, N.
dc.contributor.editorJulien, Mayor
dc.contributor.editorRuh, Nicholas
dc.contributor.editorPlunkett, Kim
dc.date.accessioned2011-10-13T08:01:09Z
dc.date.available2011-10-13T08:01:09Z
dc.date.issued2009
dc.identifier.citationCalcraft , L , Adams , R & Davey , N 2009 , The performance pf sparsley-connected 2D associative memory models with non-random images . in M Julien , N Ruh & K Plunkett (eds) , Connectionist Models of Behavior and Cognition: Proceedings of the 11th Neural Computation and Psychology Workshop : (Progress in Neural Processing) . vol. 18 , World Scientific Publishing , pp. 103-113 , Proceedings of the 11th Neural Computation and Psychology Worksho , Oxford , United Kingdom , 16/07/08 . https://doi.org/10.1142/9789812834232_0009
dc.identifier.citationconference
dc.identifier.isbn981-283-422-2
dc.identifier.isbn978-981-283-422-5
dc.identifier.otherPURE: 410340
dc.identifier.otherPURE UUID: e306988c-98da-4887-87de-7cdf64b68b1d
dc.identifier.otherWOS: 000268630000009
dc.identifier.urihttp://hdl.handle.net/2299/6672
dc.descriptionOriginal conference paper can be found at: http://eproceedings.worldscinet.com/ Copyright World Scientific Publishing [Full text of this paper is not available in the UHRA]
dc.description.abstractA sparsely connected associative memory model is built with small-world connectivity, and trained on both random, and real-world image sets. It is found that pattern recall using real-world images can vary significantly from that of random images, and that the relationship between network wiring strategy and performance changes dramatically when training sets consist of certain types of real-world image.en
dc.format.extent11
dc.language.isoeng
dc.publisherWorld Scientific Publishing
dc.relation.ispartofConnectionist Models of Behavior and Cognition: Proceedings of the 11th Neural Computation and Psychology Workshop
dc.titleThe performance pf sparsley-connected 2D associative memory models with non-random imagesen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
rioxxterms.versionofrecordhttps://doi.org/10.1142/9789812834232_0009
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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