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Support vector machines study on english isolated-word-error classification and regression

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dc.citedby1
dc.contributor.authorHasan A.B.en_US
dc.contributor.authorKiong T.S.en_US
dc.contributor.authorPaw J.K.S.en_US
dc.contributor.authorZulkifle A.K.en_US
dc.contributor.authorid55378583800en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid7801341335en_US
dc.date.accessioned2023-12-29T07:45:12Z
dc.date.available2023-12-29T07:45:12Z
dc.date.issued2013
dc.description.abstractA better understanding on word classification and regression could lead to a better detection and correction technique. We used different features or attributes to represent a machine-printed English word and support vector machines is used to evaluate those features into two class types of word: correct and wrong word. Our proposed support vectors model classified the words by using fewer words during the training process because those training words are to be considered as personalized words. Those wrong words could be replaced by correct words predicted by the regression process. Our results are very encouraging when compared with neural networks, Hamming distance or minimum edit distance technique; with further improvement in sight. � Maxwell Scientific Organization, 2013.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.19026/rjaset.5.4985
dc.identifier.epage537
dc.identifier.issue2
dc.identifier.scopus2-s2.0-84872775017
dc.identifier.spage531
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84872775017&doi=10.19026%2frjaset.5.4985&partnerID=40&md5=2de99a6ce8a899c8fd7544beea1540b1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30170
dc.identifier.volume5
dc.pagecount6
dc.publisherMaxwell Science Publicationsen_US
dc.relation.ispartofAll Open Access; Hybrid Gold Open Access
dc.sourceScopus
dc.sourcetitleResearch Journal of Applied Sciences, Engineering and Technology
dc.subjectArtificial intelligence
dc.subjectCommunication
dc.subjectStatistical theory
dc.subjectSVM kernel
dc.subjectArtificial intelligence
dc.subjectChemical detection
dc.subjectCommunication
dc.subjectHamming distance
dc.subjectRegression analysis
dc.subjectCorrection techniques
dc.subjectEnglish word
dc.subjectMinimum edit distance
dc.subjectStatistical theory
dc.subjectSupport vector
dc.subjectSVM kernel
dc.subjectTraining process
dc.subjectWord classification
dc.subjectSupport vector machines
dc.titleSupport vector machines study on english isolated-word-error classification and regressionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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