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.

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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
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
Collection: School of Engineering and Information Technology
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