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Reseach Article

Classification using Different Normalization Techniques in Support Vector Machine

Published on October 2013 by Priti Sudhir Patki, Vishakha V. Kelkar
International Conference on Communication Technology
Foundation of Computer Science USA
ICCT - Number 2
October 2013
Authors: Priti Sudhir Patki, Vishakha V. Kelkar
fe3ce4ea-bc33-4a5c-ad3c-6b68f26e5fd2

Priti Sudhir Patki, Vishakha V. Kelkar . Classification using Different Normalization Techniques in Support Vector Machine. International Conference on Communication Technology. ICCT, 2 (October 2013), 4-6.

@article{
author = { Priti Sudhir Patki, Vishakha V. Kelkar },
title = { Classification using Different Normalization Techniques in Support Vector Machine },
journal = { International Conference on Communication Technology },
issue_date = { October 2013 },
volume = { ICCT },
number = { 2 },
month = { October },
year = { 2013 },
issn = 0975-8887,
pages = { 4-6 },
numpages = 3,
url = { /proceedings/icct/number2/13651-1313/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Communication Technology
%A Priti Sudhir Patki
%A Vishakha V. Kelkar
%T Classification using Different Normalization Techniques in Support Vector Machine
%J International Conference on Communication Technology
%@ 0975-8887
%V ICCT
%N 2
%P 4-6
%D 2013
%I International Journal of Computer Applications
Abstract

Classification is one of the most important tasks for different application such as text categorization, tone recognition, image classification, data classification etc. The Support Vector Machine is a popular classification technique. In this paper we have performed different normalization techniques on different datasets. These techniques help in obtaining high training accuracy for classification. The classification is performed on these datasets using SVM.

References
  1. C. J. Burges, "A tutorial on support vector machines for pattern recognition," in Data mining and knowledge discovery, U. Fayyad,Ed. Kluwer Academic, 1998, pp. 1–43.
  2. Miloš Kovaevi, Branislav Bajat, Branislav Trivi, Radmila Pavlovi, "Geological Units Classification of Multispectral Images by Using Support Vector Machines", 2009 International Conference on Intelligent Networking and Collaborative Systems, 978-0-7695-3858-7/09.
  3. S. V. S Prasad, Dr. T. Satya Savitri, Dr. I. V. Murali Krishna, "Classification of Multispectral Satellite Images using Clustering with SVM Classifier", International Journal of Computer Applications (0975 – 8887) Volume 35– No. 5, December 2011.
  4. Helmi Zuihaidi Mohd Shafri, Affendi Suhaili, Shattri Mansor, "The Performance of Maximum Likelihood, Spectral Angle Mapper, Neural Network and Decision Tree Classifiers in Hyperspectral Image Analysis", Journal of Computer Science 3 (6): 419-423, 2007, ISSN 1549-3636.
  5. Vapnik, V. , 1998. Statistical Learning Theory. Wiley Publications, New York.
  6. C. Huang, L. S. Davis, and J. R. G. Townshend, "An assessment of support vector machines for land cover classification," Int. J. Remote sensing, vol. 23, no. 4, pp. 725–749, 2002.
  7. Pabitra Mitra *, B. Uma Shankar, Sankar K. Pal, Pattern Recognition Letters 25 (2004) 1067–1074.
  8. Gr´egoire Mercier and Marc Lennon, "Support Vector Machines for Hyperspectral Image Classification with Spectral-based kernels," IEEE Transactions 2003, 0-7803-7930-6.
  9. Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin, "A Practical Guide to Support Vector Classification", Dept of Computer Science National Taiwan Uni, Taipei, 106, Taiwan.
  10. Asa Ben-Hur and Jason Weston, "A User's Guide to Support Vector Machines", O. Carugo, F. Eisenhaber (eds. ), Data Mining Techniques for the Life Sciences, Methods in Molecular Biology 609, DOI 10. 1007/978-1-60327-241-4_13, Humana Press, a part of Springer Science+Business Media, LLC 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Classification Normalization Support Vector Machine Kernel Functions.