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2013 (Vol. 5, Issue: 02)
Article Information:

Support Vector Machines Study on English Isolated-Word-Error Classification and Regression

Abu Bakar Hasan, Tiong Sieh Kiong, Johnny Koh Siaw Paw and Ahmad Kamal Zulkifle
Corresponding Author:  Abu Bakar Hasan 

Key words:  Artificial intelligence, communication, statistical theory, SVM kernel, , ,
Vol. 5 , (02): 531-537
Submitted Accepted Published
May 13, 2012 May 29, 2012 January 11, 2013
Abstract:

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 neural networks, Hamming distance or minimum edit distance technique; with further improvement in sight.
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  Cite this Reference:
Abu Bakar Hasan, Tiong Sieh Kiong, Johnny Koh Siaw Paw and Ahmad Kamal Zulkifle, 2013. Support Vector Machines Study on English Isolated-Word-Error Classification and Regression.  Research Journal of Applied Sciences, Engineering and Technology, 5(02): 531-537.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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