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dc.contributor.convenorYasushi Yagi (Osaka Univ.)
dc.contributor.authorKunwar, Rituraj
dc.contributor.authorPal, Umapada
dc.contributor.authorBlumenstein, Michael
dc.date.accessioned2017-05-03T16:09:15Z
dc.date.available2017-05-03T16:09:15Z
dc.date.issued2013
dc.date.modified2014-05-09T02:57:25Z
dc.identifier.refurihttp://www.am.sanken.osaka-u.ac.jp/ACPR2013/index.html
dc.identifier.doi10.1109/ACPR.2013.138
dc.identifier.urihttp://hdl.handle.net/10072/59073
dc.description.abstract-This paper addresses the problem of creating a handwritten character recogniser, which makes use of both labelled and unlabelled data to learn continuously over time to make the recogniser adaptable. The proposed method makes learning possible from a continuous in?ow of a potentially unlimited amount of data without the requirement for storage. It highlights the use of unlabelled data for better parameter estimation, especially when labelled data is scarce and expensive unlike unlabelled data. We introduce an algorithm for learning from labelled and unlabelled samples based on the combination of novel online ensemble of the Randomized Naive Bayes classi?ers and a novel incremental variant of the Expectation Maximization (EM) algorithm. We make use of a weighting factor to modulate the contribution of unlabelled data. An empirical evaluation of the proposed method on Tamil handwritten base character recognition proves ef?cacy of the proposed method to carry out incremental semi-supervised learning and producing accuracy comparable to state-of-the-art batch learning method. Online handwritten Tamil characters from the IWFHR 2006 competition dataset was used for evaluating the proposed method.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent177412 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.publisherIEEE
dc.publisher.placeUnited States
dc.publisher.urihttp://www.am.sanken.osaka-u.ac.jp/ACPR2013/index.html
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofconferencenameACPR 2013
dc.relation.ispartofconferencetitle2nd IAPR Asian Conference on Pattern Recognition (ACPR2013) , proceedings
dc.relation.ispartofdatefrom2013-11-05
dc.relation.ispartofdateto2013-11-08
dc.relation.ispartoflocationOkinawa, Japan
dc.rights.retentionY
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchcode080109
dc.titleSemi-Supervised Online Learning of Handwritten Characters Using a Bayesian Classifier
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.date.issued2013
gro.hasfulltextFull Text
gro.griffith.authorBlumenstein, Michael M.
gro.griffith.authorKunwar, Rituraj


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