A novel episodic associative memory model for enhanced classification accuracy Wickramasinghe, L., Alahakoon, D. and Smith-Miles, Kate 2007, A novel episodic associative memory model for enhanced classification accuracy, Pattern recognition letters, vol. 28, no. 10, pp. 1193-1202, doi: 10.1016/j.patrec.2007.02.012. Attached Files Name Description MIMEType Size Downloads Title A novel episodic associative memory model for enhanced classification accuracy Author(s) Wickramasinghe, L. Alahakoon, D. Smith-Miles, Kate Journal name Pattern recognition letters Volume number 28 Issue number 10 Start page 1193 End page 1202 Publisher Elsevier BV Place of publication Amsterdam, Netherlands Publication date 2007-07-15 ISSN 0167-8655 1872-7344 Keyword(s) cognitive models associative memory single pass learning online learning Summary A novel approach to Episodic Associative Memory (EAM), known as Episodic Associative Memory with a Neighborhood Effect (EAMwNE) is presented in this paper. It overcomes the representation limitations of existing episodic memory models and increases the potential for their use in practical application. Notes Available online 28 February 2007 Language eng DOI 10.1016/j.patrec.2007.02.012 Field of Research 080109 Pattern Recognition and Data Mining HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2007, Elsevier B.V. Persistent URL http://hdl.handle.net/10536/DRO/DU:30007653 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Engineering and Information Technology Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 20 Feb 2009, 08:27:54 EST Fri, 19 Aug 2011, 15:26:31 EST Fri, 19 Aug 2011, 15:27:35 EST Mon, 16 Jun 2014, 08:58:44 EST Fri, 10 Oct 2014, 08:21:30 EST Filtered Full Citation counts:   Cited 7 times in TR Web of Science Cited 8 times in Scopus Search Google Scholar Access Statistics: 488 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Mon, 29 Sep 2008, 08:54:35 EST