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Cited by 17

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A survey of cross-domain text categorization techniques

Ramakrishna Murty, M., Murthy, J.V.R, Prasad Reddy, PVGD, Satapathy, S.C.·2012·2012 1st International Conference on Recent Advances in Information Technology (RAIT)
DOI 10.1109/rait.2012.6194629
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Abstract

Text Mining is important, emerging, research area, because plenty of text resources growing rapidly through the internet and digital world. In the text data analysis text categorization is one of the vital techniques. Traditional text categorization methods are not able to handle well with learning across different domains. Cross-domain classification is more challenging problem than single domain classification problem. In this paper survey of cross-domain text categorization techniques have been presented.

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Research works printed but not linked: 15

  1. bornam, 2009, The Expectation Maximization Algorithm a Short Tutorial
  2. 2011, International Journal of Computer Applications, 27
  3. konchady, 2007, Text Mining Application Programming
  4. 10.1162/jmlr.2003.3.4-5.993 · DOI
  5. ramakrishna murty, 0, Text Document Classification Base-on Least-square Support Vector Machines with Singular Value
  6. lex, 2010, Efficient cross-domain classification of weblogs, International Journal of Intelligent Computing Research (IlICR), 1
  7. upasana, 2010, A survey of text classification techniques for E-mail filtering, Second International Conference on Machine Learning and Computing"
  8. gupta, 2011, New Framework for Cross-domain Document Classification
  9. pu, 2008, Using wikipedia for co-clustering based cross-domain text classification, Eighth IEEEInternational Conference on Data Mining
  10. hovold, 2005, Nai?ve bayes spam filtering using word position based attributes, International Conference of E-mail and Anti Spam
  11. 10.1145/1390334.1390441 · DOI
  12. pu, 2008, Cross-domain Text Classification Using Wikipedia, 9
  13. wan, 2010, Bi-weighting domain adaptation for cross-language text classification, Proceedings of the Twenty Second International Joint Conference on Artificial Intelligence
  14. wajeed, 0, Text classification using machine learning, Journal of Theoretical and Applied Information Technology, 2005
  15. 10.1145/956755.956764 · DOI

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Metadata

Authors
Ramakrishna Murty, M., Murthy, J. V. R., Prasad Reddy, P., & Satapathy, S. C.
Venue
2012 1st International Conference on Recent Advances in Information Technology (RAIT)
Publisher
IEEE
Published
2012-03
Pages
499-504
DOI
10.1109/rait.2012.6194629
Cited by
17
References
0

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