An improvement of one-against-one method for multi-class support vector machine

Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007

Volumn 5, Issue , 2007, Pages 2915-2920

  Liu, Yang ^a   Wang, Rui ^a   Zeng, Ying Sheng ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

Multi class problems; One against one method; Support vector machine (SVM)

Indexed keywords

MULTI-CLASS PROBLEMSS; ONE-AGAINST-ONE METHODS;

CLASSIFICATION (OF INFORMATION); NUMERICAL METHODS; PROBLEM SOLVING;

SUPPORT VECTOR MACHINES;

EID: 38049002505     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLC.2007.4370646     Document Type: Conference Paper

References (16)

1. Cristianini N, Shawe-Taylor J (2000) "An introduction to support vector machines and other kernel-based learning methods", Cambridge University Press, Cambridge, UK
2. Vapnik VN (1998) "Statistical learning theory", Wiley, New York
3. V.Vapnik, S. Golowich, and A. Smola, "Support vector method for function approximation, regression estimation and signal processing", in Advances in Neural Information Processing Systems. Cambridge, MIT Press, 1997, vol. 9
4. C. J. Burges, "A tutorial on support vector machines for pattern recognition", Knowledge Discovery and Data Mining, vol. 2, pp. 121-167, June 1998
5. Platt JC, Cristianini N, Shawe-Taylor J (2000) "Large margin DAGs for multi-class classification ", In: Advances in neural information processing systems, vol 12, MIT Press, Cambridge, MA, pp 547-553
6. Hsu C-W, Lin C-J (2002) "A comparison of methods for multi-class support vector machines ", IEEE Trans Neural Networks, 13:415-425
7. Ryan Rifkin, Aldebaro Klautau, "In defense of one-vs-all classification", Journal of Machine Learning Research 5 (2004) 101-141
8. L. Khan, and Q. Chen, "Effective Intrusion Detection Using Support Vector Machines", In South Central Information Security Symposium, SCISS, Rice University, Houston, Texas, April 2004, pp. 8
9. C. Bregler and S. Omohundro. "Nonlinear manifold learning for visual speech recognition", in Proc. IEEE ICCV, 1995, pp. 494-499.
10. Miguel Ángel Sotelo, Jesús Nuevo, David Fernández, I. Parra, Luis Miguel Bergasa, Manuel Ocaña, and Ramón Flores, "SVM-based Obstacles Recognition for Road Vehicle Applications", IJCAI 2005, pp 1740-1741
11. C. Cortes and V. Vapnik, "Support-vector networks", Mach. Learn., vol. 20, pp. 273-297, 1995
12. V. Vapnik, "The nature of statistical learning theory", New York: Springer-Verlag, 1995
13. Krebel UH-G (1998) "Pairwise classification and support vector machines", In: Scholkopf B, Burges C, Somla A (eds) Advances in kernel methods: support vector machine. MIT Press, Cambrige, MA, pp 255-268
14. J. Friedman. (1996) "Another Approach to Polychotomous Classification ", Dept. Statist., Stanford Univ., Stanford, CA.
15. http://asi.insa-rouen.fr/~arakotom/toolbox/
16. http://download.joachims.org/svm_multiclass/examples/example4.tar.gz