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dc.contributor.authorCalcraft, L.
dc.contributor.authorAdams, R.G.
dc.contributor.authorChen, W.
dc.contributor.authorDavey, N.
dc.date.accessioned2008-10-01T13:57:09Z
dc.date.available2008-10-01T13:57:09Z
dc.date.issued2008
dc.identifier.citationCalcraft , L , Adams , R G , Chen , W & Davey , N 2008 , Using graph theoretic measures to predict the performance of associative memory models . in ESANN2008: 16th European Symposium on Artificial Neural Networks . ESANN , pp. 107-112 .
dc.identifier.isbn2-930-307080
dc.identifier.otherPURE: 84126
dc.identifier.otherPURE UUID: ed66b94c-c638-4452-926f-4eba5bb00e07
dc.identifier.otherdspace: 2299/2415
dc.identifier.otherScopus: 84887014605
dc.identifier.urihttp://hdl.handle.net/2299/2415
dc.descriptionOriginal paper can be found at: http://www.dice.ucl.ac.be/esann/proceedings/electronicproceedings.htm
dc.description.abstractWe test a selection of associative memory models built with different connection strategies, exploring the relationship between the structural properties of each network and its pattern-completion performance. It is found that the Local Efficiency of the network can be used to predict pattern completion performance for associative memory models built with a range of different connection strategies. This relationship is maintained as the networks are scaled up in size, but breaks down under conditions of very sparse connectivity.en
dc.language.isoeng
dc.publisherESANN
dc.relation.ispartofESANN2008: 16th European Symposium on Artificial Neural Networks
dc.titleUsing graph theoretic measures to predict the performance of associative memory modelsen
dc.contributor.institutionSchool of Computer Science
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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