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dc.contributor.authorKunwar, Rituraj
dc.contributor.authorPal, Umapada
dc.contributor.authorBlumenstein, Michael
dc.contributor.editorAnders Heyden, Denis Laurendeau
dc.date.accessioned2017-06-21T12:30:53Z
dc.date.available2017-06-21T12:30:53Z
dc.date.issued2014
dc.identifier.refurihttp://www.icpr2014.org/index.htm
dc.identifier.doi10.1109/ICPR.2014.535
dc.identifier.urihttp://hdl.handle.net/10072/68702
dc.description.abstractThis work addresses the problem of creating a Bayesian Network based online semi-supervised handwritten character recognisor, which learns continuously over time to make a adaptable recognisor. The proposed method makes learning possible from a continuous inflow of a potentially unlimited amount of data without the requirement for storage. It highlights the use of unlabelled data for boosting the accuracy, especially when labelled data is scarce and expensive unlike unlabelled data. An algorithm is introduced to perform semi-supervised learning based on the combination of novel online ensemble of the Randomized Bayesian network classifiers and a novel online variant of the Expectation Maximization (EM) algorithm. We make use of a novel varying weighting factor to modulate the contribution of unlabelled data. Proposed method was evaluated using online handwritten Tamil characters from the IWFHR 2006 competition dataset. The accuracy obtained was comparable to the state of the art batch learning methods like HMM and SVMs.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.publisherIEEE
dc.publisher.placeUnited States
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencenameICPR 2014
dc.relation.ispartofconferencetitlePattern Recognition (ICPR), 2014 22nd International Conference on
dc.relation.ispartofdatefrom2014-08-24
dc.relation.ispartofdateto2014-08-28
dc.relation.ispartoflocationStockholm, Sweden
dc.rights.retentionY
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchcode080199
dc.titleSemi-Supervised Online Bayesian Network Learner for Handwritten Characters Recognition
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorBlumenstein, Michael M.
gro.griffith.authorKunwar, Rituraj


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

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