Earticle

초록

영어

This paper mainly explores the use of Support Vector Machines (SVMs) for Uyghur text classification, presents the process of text categorization: Text preprocessing, feature dimensionality reduction, representation method and classification of text features etc., discusses the SVMs classification algorithm in the application of Uyghur text classification. Focus on the construction of text categorization model and its procedures. Experiment results show that training by using the selected training data with the guarantee of the performance of the classifier, has higher efficiency than other nearest neighbor classifier (KNN), Naive Bayes (NB) classifier with increased accuracy.

목차

Abstract
 1. Introduction
 2. Text Categorization
 3. Main Title
  3.1 The Features of Uyghur Language
  3.2 Pre-processing of Uyghur Texts
  3.3 Feature Selection
 4. The SVM based Text Classification Algorithm
 5. Experimental Results and Analysis
  5.1 Experimental Corpus
  5.2 Experimental Environment
  5.3 Results of Classification based on the SVM
  5.4 Comparison Results of SVM, KNN and NB
 6. Conclusions
 Acknowledgements
 References

키워드

저자

• Palidan Tuerxun [ School of information and technology, Northwestern University, Xi’an, China, School of Software, Xinjiang University, Urumqi, Xinjiang, China ]
• Fang Dingyi [ School of information and technology, Northwestern University, Xi’an, China ]
• Askar Hamdulla [ School of Software, Xinjiang University, Urumqi, Xinjiang, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605