Research Article | OPEN ACCESS
Support Vector Machines Study on English Isolated-Word-Error Classification and Regression
1Abu Bakar Hasan, 1Tiong Sieh Kiong, 1Johnny Koh Siaw Paw and 2Ahmad Kamal Zulkifle
1College of Engineering
2College of Foundation and General Studies, Universiti Tenaga Nasional, 43009 Kajang, Malaysia
Research Journal of Applied Sciences, Engineering and Technology 2013 2:531-537
Received: May 13, 2012 | Accepted: May 29, 2012 | Published: 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.
Keywords:
Artificial intelligence, communication, statistical theory, SVM kernel,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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