A novel framework for multi-class classification via ternary smooth support vector machine

Pattern Recognition

Volumn 44, Issue 6, 2011, Pages 1235-1244

  Chang, Chih Cheng ^a   Chien, Li Jen ^a   Lee, Yuh Jye ^a  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Confidence; Hidden classes; Multi class classification; Smooth method; Support vector machine; Ternary voting games

Indexed keywords

CONFIDENCE; HIDDEN CLASSES; MULTI-CLASS CLASSIFICATION; SMOOTH METHOD; TERNARY VOTING GAMES;

ERROR ANALYSIS; EXPERIMENTS; SUPPORT VECTOR MACHINES; VECTORS;

CLASSIFICATION (OF INFORMATION);

EID: 79551545319     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2010.11.016     Document Type: Article

References (36)

1. E.L. Allwein, R.E. Schapire, and Y. Singer Reducing multiclass to binary: a unifying approach for margin classifiers Journal of Machine Learning Research 1 2000 113 141
2. A. Asuncion, D.J. Newman, UCI machine learning repository, University of California, School of Information and Computer Science, Irvine, CA, 〈 http://www.ics.uci.edu/~mlearn/MLRepository.html 〉, 2007.
3. L. Bottou, C. Cortes, J. Denker, H. Drucker, I. Guyon, L. Jackel, Y. LeCun, U. Muller, E. Sackinger, P. Simard, and V. Vapnik Comparison of classifier methods: a case study in handwriting digit recognition International Conference on Pattern Recognition 1994 IEEE Computer Society Press 77 87
4. E. Bredensteiner, and K.P. Bennett Multicategory classification by support vector machines Computational Optimization and Applications 12 1999 53 79
5. C.J.C. Burges A tutorial on support vector machines for pattern recognition Data Mining and Knowledge Discovery 2 1998 121 167
6. C.C. Chang, C.J. Lin, LIBSVM: a library for support vector machines, Software available at 〈 http://www.csie.ntu.edu.tw/~cjlin/libsvm 〉, 2001.
7. F.H. Clarke Optimization and Nonsmooth Analysis 1990 SIAM Philadelphia, PA
8. C. Cortes, and V. Vapnik Support vector networks Machine Learning 20 1995 273 297
9. K. Crammer, and Y. Singer Improved output coding for classification using continuous relaxation Proceedings of the Thirteenth Annual Conference on Neural Information Processing Systems 2000
10. K. Crammer, and Y. Singer On the learnability and design of output codes for multiclass problems Machine Learning 47 2002 201 233
11. N. Cristianini, and J. Shawe-Taylor An Introduction to Support Vector Machines 2000 Cambridge University Press Cambridge
12. T.G. Dietterich, and B. Bakiri Solving multiclass learning problems via error-correcting output codes Journal of Artificial Intelligence Research 2 1995 263 286
13. K.T. Fang, and Y. Wang Number-Theoretic Methods in Statistics 1994 Chapman & Hall London
14. D.S. Felsenthal, and M. Machover Ternary voting games International Journal of Game Theory 26 1997 335 351
15. J. Friedman, Another approach to polychotomous classification Technical Report, Department of Statistics, Stanford University, 1996.
16. J. Frnkranz Round robin classification Journal of Machine Learning Research 2002 721 747
17. C.W. Hsu, and C.J. Lin A comparison on methods for multi-class support vector machines IEEE Transactions on Neural Networks 13 2002 415 425
18. C.M. Huang, Y.J. Lee, D.K.J. Lin, and S.Y. Huang Model selection for support vector machines via uniform design A Special Issue on Machine Learning and Robust Data Mining of Computational Statistics and Data Analysis 52 2007 335 346
19. E. Hllermeier, and S. Vanderlooy Combing predictions in pairwise classification: an optimal adaptive voting strategy and its relation to weighted voting Pattern Recognition 43 2010 128 142
20. H.C. Kim, S. Pang, H.M. Je, D. Kim, and S.Y. Bang Constructing support vector machine ensemble Pattern Recognition 36 2003 2757 2767
21. S. Knerr, L. Personnaz, and G. Dreyfus Single-layer learning revisited: a stepwise procedure for building and training a neural network J. Fogelman, Neurocomputing: Algorithms, Architectures and Applications 1990 Springer-Verlag
22. U. Kreßel Pairwise classification and support vector machines B. Schlkopf, C.J.C. Burges, A.J. Smola, Advances in Kernel MethodsSupport Vector Learning 1999 MIT Press Cambridge, MA 255 268
23. Y. Lee, Y. Lin, and G. Wahba Multicategory support vector machines Proceedings of the 33rd Symposium on the Interface 2001
24. Y.J. Lee, and S.Y. Huang Reduced support vector machines: a statistical theory IEEE Transactions on Neural Networks 18 2007 1 13
25. Y.J. Lee, and O.L. Mangasarian SSVM: a smooth support vector machine Computational Optimization and Applications 20 2001 5 22
26. Y.J. Lee, O.L. Mangasarian, RSVM: reduced support vector machines, in: First SIAM International Conference on Data Mining, Chicago, 2001.
27. O.L. Mangasarian Generalized support vector machines A. Smola, P. Bartlett, B. Schlkopf, D. Schuurmans, Advances in Large Margin Classifiers 2000 MIT Press Cambridge, MA 135 146
28. MATLAB, Users Guide, The MathWorks, Inc., Natick, MA 01760, 1994-2006. 〈 http://www.mathworks.com 〉.
29. D. Michie, D.J. Spiegelhalter, and C.C. Taylor Machine Learning, Neural and Statistical Classification 1994 Prentice-Hall Englewood Cliffs, NJ
30. J.C. Platt, N. Cristianini, and J. Shawe-Taylor Large margin DAGs for multiclass classification Advances in Neural Information Processing Systems vol. 12 2000 MIT Press 547 553
31. R. Rifkin, and A. Klautau In defense of one-vs-all classification Journal of Machine Learning Research 5 2004 101 141
32. A. Smola, B. Schlkop, Sparse greedy matrix approximation for machine learning, in: Proceedings of 17th International Conference on Machine Learning, San Francisco, CA, 2000, pp. 911918.
33. V.N. Vapnik Statistical Learning Theory 1998 Wiley New York
34. J. Weston, and C. Watkins Multi-class support vector machines M. Verleysen, Proceedings of ESANN99 1999 Brussels D Facto Press 219 224
35. Y.-C.F. Wang, and D. Casasent New support vector-based design method for binary hierarchical classifiers for multi-class classification problems Neural Networks 21 2008 502 510
36. C.K.I. Williams, and M. Seeger Using the Nystrm method to speed up kernel machines T.K. Leen, T.G. Dietterich, V. Tresp, Advances in Neural Information Processing Systems, vol. 13 2001 MIT Press Cambridge, MA, USA 682 688