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dc.contributor.convenorUniversity of Tasmaniaen_AU
dc.contributor.authorThornton, Johnen_US
dc.contributor.authorGustafsson, Torbjornen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorHine, Trevoren_US
dc.contributor.editorAbdul Sattar and Byeong-Ho Kangen_US
dc.date.accessioned2017-04-24T09:59:02Z
dc.date.available2017-04-24T09:59:02Z
dc.date.issued2006en_US
dc.date.modified2009-09-28T06:50:17Z
dc.identifier.refurihttp://www.comp.utas.edu.au/ai06/en_AU
dc.identifier.doihttp://www.comp.utas.edu.au/ai06/en_AU
dc.identifier.urihttp://hdl.handle.net/10072/13103
dc.description.abstractThere is increasing evidence to suggest that the neocortex of the mammalian brain does not consist of a collection of specialised and dedicated cortical architectures, but instead possesses a fairly uniform, hierarchically organised structure. As Mountcastle has observed [1], this uniformity implies that the same general computational processes are performed across the entire neocortex, even though different regions are known to play different functional roles. Building on this evidence, Hawkins has proposed a top-down model of neocortical operation [2], taking it to be a kind of pattern recognition machine, storing invariant representations of neural input sequences in hierarchical memory structures that both predict sensory input and control behaviour. The first partial proof of concept of Hawkins' model was recently developed using a hierarchically organised Bayesian network that was tested on a simple pattern recognition problem [3]. In the current study we extend Hawkins' work by comparing the performance of a backpropagation neural network with our own implementation of a hierarchical Bayesian network in the well-studied domain of character recognition. The results show that even a simplistic implementation of Hawkins' model can produce recognition rates that exceed a standard neural network approach. Such results create a strong case for the further investigation and development of Hawkins' neocortically-inspired approach to building intelligent systems.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent14088 bytes
dc.format.extent122096 bytes
dc.format.mimetypetext/plain
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherSpringer-Verlagen_US
dc.publisher.placeBerlinen_US
dc.publisher.urihttp://www.springer.com/east/home?SGWID=5-102-0-0-0&referer=www.springeronline.comen_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencename19th Australian Joint Conference on Artificial Intelligenceen_US
dc.relation.ispartofconferencetitleAI 2006: Advances in Artificial Intelligenceen_US
dc.relation.ispartofdatefrom2006-12-04en_US
dc.relation.ispartofdateto2006-12-08en_US
dc.relation.ispartoflocationHobarten_US
dc.rights.retentionNen_AU
dc.subject.fieldofresearchcode280207en_US
dc.titleRobust Character Recognition Using a Hierarchical Bayesian Networken_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 2006 Springer. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.comen_AU
gro.date.issued2006
gro.hasfulltextFull Text


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    Contains papers delivered by Griffith authors at national and international conferences.

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