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
![[img]](http://eprints.utem.edu.my/style/images/fileicons/application_pdf.png)
|
PDF
663-672.pdf - Published Version Download (449kB) |
Abstract
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.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > Q Science (General) |
| Divisions: | Faculty of Electronics and Computer Engineering > Department of Telecommunication Engineering |
| Depositing User: | Dr David Yap |
| Date Deposited: | 20 Aug 2013 03:33 |
| Last Modified: | 28 May 2015 04:01 |
| URI: | http://eprints.utem.edu.my/id/eprint/9099 |
| Statistic Details: | View Download Statistic |
Actions (login required)
 |
View Item |