Fine tuning on support vector regression parameters for personalized english word-error correction algorithm
A 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: c...
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2023
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| author | Hasan A.B. Kiong T.S. Paw J.K.S. Tasrip E. Azmi M.S.M. |
| author2 | 55378583800 |
| author_facet | 55378583800 Hasan A.B. Kiong T.S. Paw J.K.S. Tasrip E. Azmi M.S.M. |
| author_sort | Hasan A.B. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | A 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 Microsoft's spell checker, and further improvement is in sight. |
| format | Article |
| id | my.uniten.dspace-30249 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| record_format | dspace |
| spelling | my.uniten.dspace-302492023-12-29T15:45:54Z Fine tuning on support vector regression parameters for personalized english word-error correction algorithm Hasan A.B. Kiong T.S. Paw J.K.S. Tasrip E. Azmi M.S.M. 55378583800 15128307800 22951210700 55378068700 36994351200 Artificial intelligence FPGA Statistical theory Support vector machines A 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 Microsoft's spell checker, and further improvement is in sight. Final 2023-12-29T07:45:54Z 2023-12-29T07:45:54Z 2012 Article 2-s2.0-84867162827 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867162827&partnerID=40&md5=01aa49f137f9eb7ebd4d67e2a5a389fe https://irepository.uniten.edu.my/handle/123456789/30249 6 6 15 20 Scopus |
| spellingShingle | Artificial intelligence FPGA Statistical theory Support vector machines Hasan A.B. Kiong T.S. Paw J.K.S. Tasrip E. Azmi M.S.M. Fine tuning on support vector regression parameters for personalized english word-error correction algorithm |
| title | Fine tuning on support vector regression parameters for personalized english word-error correction algorithm |
| title_full | Fine tuning on support vector regression parameters for personalized english word-error correction algorithm |
| title_fullStr | Fine tuning on support vector regression parameters for personalized english word-error correction algorithm |
| title_full_unstemmed | Fine tuning on support vector regression parameters for personalized english word-error correction algorithm |
| title_short | Fine tuning on support vector regression parameters for personalized english word-error correction algorithm |
| title_sort | fine tuning on support vector regression parameters for personalized english word-error correction algorithm |
| topic | Artificial intelligence FPGA Statistical theory Support vector machines |
| url_provider | http://dspace.uniten.edu.my/ |