CFP last date
22 July 2024
Reseach Article

Analysis of Machine Learning through Support Vector Machine: Catalyst

by Pooja Shrimali, K. Venugopalan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 6
Year of Publication: 2014
Authors: Pooja Shrimali, K. Venugopalan

Pooja Shrimali, K. Venugopalan . Analysis of Machine Learning through Support Vector Machine: Catalyst. International Journal of Computer Applications. 100, 6 ( August 2014), 42-46. DOI=10.5120/17532-8105

@article{ 10.5120/17532-8105,
author = { Pooja Shrimali, K. Venugopalan },
title = { Analysis of Machine Learning through Support Vector Machine: Catalyst },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 6 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 42-46 },
numpages = {9},
url = { },
doi = { 10.5120/17532-8105 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:29:16.693946+05:30
%A Pooja Shrimali
%A K. Venugopalan
%T Analysis of Machine Learning through Support Vector Machine: Catalyst
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 6
%P 42-46
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

This paper investigates the use of support vector machine (SVM) in machine learning. The purpose of this study is to experiment of SVM in e-learning methodology. Main constituent of this research is to innovate and implement pedagogical hypermedia document. In the article [19] artificial neural network (ANN) has been used to test learners learning capabilities, which is now being replaced by SVM in the present article to understand statistical analysis of learner's knowledge level. By this experiment it is suggested that this methodology is over and above ANN which is used as mathematical and statistical results.

  1. Abdul Rahim Ahmad1 Marzuki Khalid2 Rubiyah Yusof2 "Machine Learning Using Support Vector Machines" 1Universiti Tenaga Nasional Km 7, Jalan Kajang-Puchong, 43009 Kajang, Selangor. abdrahim@uniten. edu. my Centre for Artificial Intelligence and Robotics Universiti Teknologi Malaysia Jalan Semarak, 54100 Kuala Lumpur {marzuki,rubiyah}@utmkl. utm. my
  2. B. E. Boser, I. M. Guyon, and V. N. Vapnik "A Training Algorithm for Optimal Margin Classifiers. " In D. Haussler, editor, 5th Annual ACM Workshop on COLT, pages 144-152, Pittsburgh, PA, 1992. ACM Press.
  3. Cali_, M. E. " Relational learning techniques for natural language information extraction. " PhD thesis, University of Texas at Austin, 1998
  4. Chieu, H. L. , Ng, H. T. "Named entity recognition: A maximum entropy approach using global information. " In Proceedings of the 19th International Conference on Computational Linguistics (COLING'02), Taipei, Taiwan, 2002
  5. Ciravegna, F. : (LP)2, "an adaptive algorithm for information extraction from web related texts. " In Proceedings of the IJCAI-2001 Workshop on Adaptive Text Extraction and Mining, Seattle, 2001
  6. Duda R. and Hart P. "Pattern Classification and Scene Analysis", Wiley, New York 1973.
  7. Freitag, D. "Machine Learning for Information Extraction in Informal Domains. " PhD thesis, Carnegie Mellon University, 1998.
  8. Freitag, D. , Kushmerick, N. " Boosted Wrapper Induction. " In Proceedings of AAAI 2000, 2000.
  9. Freigtag, D. , McCallum, A. K. "Information extraction with HMMs and shrinkage. " In Proceedings of Workshop on Machine Learning for Information Extraction, pages 31{36, 1999
  10. Henok Girma "A Tutorial on Support Vector Machine" Center of expermental mechanichs University of Ljubljana 2009
  11. Kan Xie " Support Vector Machine Concept and matlab build"
  12. Karampiperis , P. , and Sampson, D. (2005). "Adaptive Learning Resources Sequencing in Educational Hypermedia Systems. " Educational Technology & Society, 8(4), 128-147.
  13. Isozaki, H. , Kazawa, H. "Efficient Support Vector Classifiers for Named Entity Recognition. " In Proceedings of the 19th International Conference on Computational Linguistics (COLING'02), pages 390{396, Taipei, Taiwan, 2002
  14. MathWorks "Classperf"
  15. MathWorks "Crossvalind"
  16. May_eld, J. , McNamee, P. , Piatko, C. "Named entity recognition using hundreds of thousands of features. " InWalter Daelemans and Miles Osborne, editors, Proceedings of CoNLL-2003, pages 184{187. Edmonton, Canada, 2003.
  17. Nello Cristianini and John Shawe-Taylor, "An Introduction to Support Vector Machines and Other Kernel-based Learning Methods", Cambridge University Press, 2000.
  18. Norsham Idris1, Norazah Yusof2, Siti Zaiton Mohd. Hashim3 "A Model for Adaptive Selection of Learning Material in an Intelligent Learning System using combination of Supervised and Unsupervised Machine Learning Techniques" Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, MALAYSIA
  19. Pooja Shrimali, K. Venugopalan, Prabha Vajpeyee "Study and Analysis of an Adaptive Web Based ELearning System" IJCA Issue 4, Volume 3 (May - July 2014) www. rspublication. com/ijca/ijca_index. htm
  20. Roth, D. , Yih, W. T. "Relational learning via propositional algorithms: an information extraction case study. " In Proceedings of the Seventeenth International Joint Conference on Arti_cial Intelligence (IJCAI), pages 1257 { 1263, 2001.
  21. Soderland, S. "Learning information extraction rules for semi-structured and free text. Machine Learning", 34 (1999) 233{272
  22. Steven Busuttil "Support Vector Machines" Department of Computer Science and AI,University of Malta
  23. Theodoros Evgenuiu and Massimilliano Pontil"Statistical Learning Theory",a Primer 1998.
  24. Vikramaditya Jakkula "Tutorial on Support Vector Machine (SVM)" Vikramaditya Jakkula, School of EECS, Washington State University, Pullman 99164.
  25. Wikipedia Online. Http://en. wikipedia. org/wiki
  26. Yaoyong Li, Kalina Bontcheva, Hamish Cunningham "SVM Based Learning System for Information Extraction. "
Index Terms

Computer Science
Information Sciences


Machine learning SVM adaptive e-learning learning objects