Support vector machines study on english isolated-word-error classification and regression

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)531-537
Number of pages7
JournalResearch Journal of Applied Sciences, Engineering and Technology
Volume5
Issue number2
Publication statusPublished - 29 Jan 2013

Fingerprint

Hamming distance
Support vector machines
Neural networks

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

Cite this

@article{cc605729559243fca1d64ecdd34f3092,
title = "Support vector machines study on english isolated-word-error classification and regression",
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.",
author = "Hasan, {Abu Bakar} and Tiong, {Sieh Kiong} and Koh, {Johnny Siaw Paw} and Zulkifle, {Ahmad Kamal}",
year = "2013",
month = "1",
day = "29",
language = "English",
volume = "5",
pages = "531--537",
journal = "Research Journal of Applied Sciences, Engineering and Technology",
issn = "2040-7459",
publisher = "Maxwell Scientific Publications",
number = "2",

}

TY - JOUR

T1 - Support vector machines study on english isolated-word-error classification and regression

AU - Hasan, Abu Bakar

AU - Tiong, Sieh Kiong

AU - Koh, Johnny Siaw Paw

AU - Zulkifle, Ahmad Kamal

PY - 2013/1/29

Y1 - 2013/1/29

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84872775017&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84872775017&partnerID=8YFLogxK

M3 - Article

VL - 5

SP - 531

EP - 537

JO - Research Journal of Applied Sciences, Engineering and Technology

JF - Research Journal of Applied Sciences, Engineering and Technology

SN - 2040-7459

IS - 2

ER -