Open Access
MATEC Web Conf.
Volume 189, 2018
2018 2nd International Conference on Material Engineering and Advanced Manufacturing Technology (MEAMT 2018)
Article Number 03009
Number of page(s) 6
Section Cloud & Network
Published online 10 August 2018
  1. Ronghui Ju, Pan Zhou, Cheng Hua Li, and Lijun Liu. (2015). An Efficient Method for Document Categorization Based on Word2vec and Latent Semantic Analysis.Computer and Information Technology; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), 2015 IEEE International Conference on. 10.1109/CIT/IUCC/DASC/PICOM.2015.336 [Google Scholar]
  2. Carmen Delgado. (2012). File Classification Scheme for Administrative Functions Common to all UN Offices. United Nations. [Google Scholar]
  3. A. Barto, et al. (2015). Learning to Act Using Real-Time Dynamic Programming.Hoboken: Rutledge Press pp.112-123. [Google Scholar]
  4. Z. Rasjid, and R. Setiawan. (2017). Performance Comparison and Optimization of Text Document Classification using k-NN and Naïve Bayes Classification Techniques. 2nd International Conference on Computer Science and Computational Intelligence 2017, ICCSCI 2017, 13-14 October 2017, Bali, Indonesia [Google Scholar]
  5. M. Malki. (2016). “Comprehensive Study and Comparison of Information Retrieval Indexing Techniques” International Journal of Advanced Computer Science and Applications (IJACSA), 7(1), 2016. [Google Scholar]
  6. L. Cavanagh. (2016). Optimizing document search using Machine Learning and Text Analytics [BlogPost]. Microsoft Azure. Retrieve January 5, 2018 from [Google Scholar]
  7. Martin Ponweiser. (2012). Latent Dirichlet Allocation in R. Institute for Statistics and Mathematics.Diploma Thesis. Retrieved December 10, 2017 from [Google Scholar]
  8. H. Chen., B. Martin, C. Daimon, and Maudsley, S. (2013).Effective use of latent semantic indexing and computational linguistics in biological and biomedical applications. Front. Physiol., 30 January 2013 | [Google Scholar]
  9. Kumar B Shravan, and V. Ravi. (2017). One-Class Text Document Classification with OCSVM and LSI. In: Dash S., Vijayakumar K., Panigrahi B., Das S. (eds) Artificial Intelligence and Evolutionary [Google Scholar]
  10. Ashwini Deshmukh, and Gayatri Hegde. (2012). A Literature Survey on Latent Semantic Indexing. International Journal of Engineering Inventions ISSN: 2278-7461, Volume 1, Issue 4 (September 2012) PP: 01-05 [Google Scholar]
  11. Duy Duc An Bui, and Zeng-Treitler. (2014). Learning Regular Expressions for Clinical Text Classification. J Am Med Inform Assoc. 2014 Sep; 21(5): 850–857. Published online 2014 Feb 27. doi:10.1136/amiajnl-2013-002411 [CrossRef] [Google Scholar]
  12. S. Hingmire, (2013). Document Classification by Topic Labeling. Conference: International Conference on Information Retrieval (SIGIR 2013), At Dublin, Ireland, Volume: pp. 877–880, ACM Press [Google Scholar]
  13. A. Janusz, W. Świeboda, A. Krasuski, and H. Nguyen, (2012). Interactive Document Indexing Method Based on Explicit Semantic Analysis. J.T. Yao et al. (Eds.): RSCTC 2012, LNAI 7413, pp. 156–165, 2012. c Springer-Verlag Berlin Heidelberg [Google Scholar]
  14. A N K Zaman, Pascal Matsakis, and Charles. Brown. (2011).Evaluation of Stop Word Lists in Text Retrieval Using Latent Semantic Indexing. 978-1-4577-1539-6/ IEEE [Google Scholar]
  15. A.P. SivaKumar, P. Premchand, and A. Govardhan. (2011). Application of Latent Semantic Indexing for Hindi-English CLIR Irrespective of Context Similarity. In: Wyld D.C., Wozniak M., Chaki N., Meghanathan N., Nagamalai D. (eds) Trends in Network and Communications. Communications in Computer and Information Science, vol 197. Springer, Berlin, Heidelberg [Google Scholar]
  16. JH Shin, M. Abebe, C.J Yoo, S. Kim, J.H Lee, and HK Yoo. (2017). Evaluating the Effectiveness of the Vector Space Retrieval Model Indexing. In: Park J., Pan Y., Yi G., Loia V. (eds) Advances in Computer Science and Ubiquitous Computing. CSA 2016, CUTE 2016, UCAWSN 2016. Lecture Notes in Electrical Engineering, vol 421. Springer, Singapore [Google Scholar]
  17. Scott Shell. (2014). An Introduction to Numpy and Scipy. Retrieved January 5, 2018 from [Google Scholar]

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