ELECTRONIC DOCUMENT CLASSIFICATION USING SUPPORT VECTOR MACHINE-AN APPLICATION FOR E-LEARNING

Sandeep Dixit, L. K.Maheshwari

2005

Abstract

Appplication of the machine learning techniques to embed adaptivity in the E-Learning frameworks is receiving considerable attention. Text Classification, or the task of automatically assigning semantic categories to natural language text, has therefore become one of the key methods for organizing digital content.Reports on SVM have mainly focussed on the theory or conceptual application of the SVM with little, if any, concern to applicability to E-Learning domain. In this paper we present a holistic approach towards building a text classifier suited for E-Learning domains. We present theory, application and use of a specific SVM software(SVM Torch) for classification of electronic documents giving every detail of how the downloaded SVM software can be used to apply the SVM concept and categorise text document in the context of E-Learning domains. Experimental results obtained by applying SVM to text document are presented. Pre-processing of the document is also presented. The experiment was conducted using SVM Torch software with ten documents for training and five documents for testing. SVMs performed the best when used with binary representation. We are confident that the extent of details provided could well serve as a useful component in E-Learning frameworks and even provide curriculum component for UG level students.

References

  1. Vapnik,V.,1995 The Nature of Statistical Learning Theory,
  2. Gunn, S.R., Brown, M., and Bossley, K.M.,1997, Network performance assessment for neurofuzzy data modeling, Intelligent Data Analysis, volume 1208 of Lecture Notes in Computer Science, pages 313-323.
  3. Vapnik,V., Golowich,S., Smola,A., 1997 Support vector method for function approximation, regression estimation, and signal processing, In M. Mozer, M. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, pages 281-287, Cambridge, MA, MIT Press.
  4. Poggio,T., Torre,V.,Koch,C.,1985 Computational vision and regularization theory, Nature, 314-319.
  5. Hadamard,J.,1923, Lectures on the Cauchy Problem in Linear Partial Differential Equations, Yale University Press.
  6. Vapnik,V., 1998, Statistical Learning Theory, Springer, N.Y.
  7. Minoux,M.,1986, Mathematical Programming: Theory and Algorithms, John Wiley and Sons.
  8. Drucker,H., Donghui Wu, Vapnik,V.N.,1999, Support Vector Machines for Spam Categorization, Proceedings of the IEEE Transaction on Neural Networks, vol. 10, no. 5, September.
  9. Aronszajn,N.,1950 Theory of reproducing kernels, Trans. Amer. Math. Soc., 686:337-404.
  10. Girosi,F.,1997, An equivalence between sparse approximation and Support Vector Machines, A.I. Memo 1606, MIT Artificial Intelligence Laboratory.
  11. Heckman, N.,1997, The theory and application of penalized least squares methods or reproducing kernel hilbert spaces made easy.
  12. Wahba,G.,1990 Spline Models for Observational Data. Series in Applied Mathematics, Vol. 59, SIAM, Philadelphia.
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Paper Citation


in Harvard Style

Dixit S. and K.Maheshwari L. (2005). ELECTRONIC DOCUMENT CLASSIFICATION USING SUPPORT VECTOR MACHINE-AN APPLICATION FOR E-LEARNING . In Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 5: EES, (ICEIS 2005) ISBN 972-8865-19-8, pages 191-198. DOI: 10.5220/0002568401910198


in Bibtex Style

@conference{ees05,
author={Sandeep Dixit and L. K.Maheshwari},
title={ELECTRONIC DOCUMENT CLASSIFICATION USING SUPPORT VECTOR MACHINE-AN APPLICATION FOR E-LEARNING},
booktitle={Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 5: EES, (ICEIS 2005)},
year={2005},
pages={191-198},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002568401910198},
isbn={972-8865-19-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Seventh International Conference on Enterprise Information Systems - Volume 5: EES, (ICEIS 2005)
TI - ELECTRONIC DOCUMENT CLASSIFICATION USING SUPPORT VECTOR MACHINE-AN APPLICATION FOR E-LEARNING
SN - 972-8865-19-8
AU - Dixit S.
AU - K.Maheshwari L.
PY - 2005
SP - 191
EP - 198
DO - 10.5220/0002568401910198