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dc.contributor.convenorAuckland University of Technology
dc.contributor.authorThornton, John
dc.contributor.authorFaichney, Jolon
dc.contributor.authorBlumenstein, Michael
dc.contributor.authorHine, Trevor
dc.contributor.editorWayne Wobcke, Mengjie Zhang
dc.date.accessioned2017-05-03T12:54:34Z
dc.date.available2017-05-03T12:54:34Z
dc.date.issued2008
dc.date.modified2010-08-30T07:04:24Z
dc.identifier.refuriwww.ai08.org
dc.identifier.doi10.1007/978-3-540-89378-3_57
dc.identifier.urihttp://hdl.handle.net/10072/23558
dc.description.abstractIn recent years, there has been a cross-fertilization of ideas between computational neuroscience models of the operation of the neo-cortex and artificial intelligence models of machine learning. Much of this work has focussed on the mammalian visual cortex, treating it as a hierarchically-structured pattern recognition machine that exploits statistical regularities in retinal input. It has further been proposed that the neocortex represents sensory information probabilistically, using some form of Bayesian inference to disambiguate noisy data. In the current paper, we focus on a particular model of the neocortex developed by Hawkins, known as hierarchical temporal memory (HTM). Our aim is to evaluate an important and recently implemented aspect of this model, namely its ability to represent temporal sequences of input within a hierarchically structured vector quantization algorithm. We test this temporal pooling feature of HTM on a benchmark of cursive hand-writing recognition problems and compare it to a current state-of-the-art support vector machine implementation. We also examine whether two pre-processing techniques can enhance the temporal pooling algorithm's performance. Our results show that a relatively simple temporal pooling approach can produce recognition rates that approach the current state-of-the-art without the need for extensive tuning of parameters. We also show that temporal pooling performance is surprisingly unaffected by the use of preprocessing techniques.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent30058 bytes
dc.format.extent171371 bytes
dc.format.mimetypetext/plain
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.publisher.placeHeidelberg, Germany
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencename21st Australasian Joint Conference on Artificial Intelligence
dc.relation.ispartofconferencetitleLecture Notes in Artificial Intelligence
dc.relation.ispartofdatefrom2008-12-03
dc.relation.ispartofdateto2008-12-05
dc.relation.ispartoflocationAuckland, New Zealand
dc.rights.retentionY
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchcode080109
dc.titleCharacter Recognition using Hierarchical Vector Quantization and Temporal Pooling
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2008 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.com
gro.date.issued2008
gro.hasfulltextFull Text
gro.griffith.authorThornton, John R.
gro.griffith.authorFaichney, Jolon B.
gro.griffith.authorBlumenstein, Michael M.
gro.griffith.authorHine, Trevor J.


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

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