Error Detection of Personalized English Isolated-Word Using Support Vector Machine
A better understanding on word classification could lead to a better detection and correction technique. In this study, a new features representation technique is used to represent the machine-printed English word. Subsequently, a well-known classification type of artificial intelligent algorithm na...
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| Format: | Article |
| Language: | en |
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Academic Journals Inc.
2012
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| Online Access: | http://eprints.utem.edu.my/id/eprint/9099/1/663-672.pdf http://eprints.utem.edu.my/id/eprint/9099/ |
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| _version_ | 1832716372737523712 |
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| author | Yap, David F. W. |
| author_facet | Yap, David F. W. |
| author_sort | Yap, David F. W. |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | A better understanding on word classification could lead to a better detection and correction technique. In this study, a new features representation technique is used to represent the machine-printed English word. Subsequently, a well-known classification type of artificial intelligent algorithm namely Support Vector Machine (SVM) is used to evaluate those features under two class types of words with proper segregation of correct and erroneous words in two data sets. Our proposed model shows good performance in error detection and is superior when compared with neural networks, Hamming distance or minimum edit distance technique; with further improvement in sight. |
| format | Article |
| id | my.utem.eprints-9099 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2012 |
| publisher | Academic Journals Inc. |
| record_format | eprints |
| spelling | my.utem.eprints-90992015-05-28T04:01:27Z http://eprints.utem.edu.my/id/eprint/9099/ Error Detection of Personalized English Isolated-Word Using Support Vector Machine Yap, David F. W. Q Science (General) A better understanding on word classification could lead to a better detection and correction technique. In this study, a new features representation technique is used to represent the machine-printed English word. Subsequently, a well-known classification type of artificial intelligent algorithm namely Support Vector Machine (SVM) is used to evaluate those features under two class types of words with proper segregation of correct and erroneous words in two data sets. Our proposed model shows good performance in error detection and is superior when compared with neural networks, Hamming distance or minimum edit distance technique; with further improvement in sight. Academic Journals Inc. 2012 Article PeerReviewed application/pdf en http://eprints.utem.edu.my/id/eprint/9099/1/663-672.pdf Yap, David F. W. (2012) Error Detection of Personalized English Isolated-Word Using Support Vector Machine. Trends in Applied Sciences Research, 7 (8). pp. 663-672. ISSN 1819-3579 10.3923/tasr.2012.663.672 |
| spellingShingle | Q Science (General) Yap, David F. W. Error Detection of Personalized English Isolated-Word Using Support Vector Machine |
| title | Error Detection of Personalized English Isolated-Word Using Support Vector Machine |
| title_full | Error Detection of Personalized English Isolated-Word Using Support Vector Machine |
| title_fullStr | Error Detection of Personalized English Isolated-Word Using Support Vector Machine |
| title_full_unstemmed | Error Detection of Personalized English Isolated-Word Using Support Vector Machine |
| title_short | Error Detection of Personalized English Isolated-Word Using Support Vector Machine |
| title_sort | error detection of personalized english isolated-word using support vector machine |
| topic | Q Science (General) |
| url | http://eprints.utem.edu.my/id/eprint/9099/1/663-672.pdf http://eprints.utem.edu.my/id/eprint/9099/ |
| url_provider | http://eprints.utem.edu.my/ |