A novel and quick SVM-based multi-class classifier

Pattern Recognition

Volumn 39, Issue 11, 2006, Pages 2258-2264

  Liu, Yiguang ^a,b   You, Zhisheng ^a   Cao, Liping ^a  

^a SICHUAN UNIVERSITY   (China)

^b UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Multi class classifier; Objective function; SVM; SVMlight approach

Indexed keywords

DATABASE SYSTEMS; DECISION THEORY; FUNCTION EVALUATION; GRAPH THEORY; LINEAR SYSTEMS; OPTIMIZATION;

MULTI-CLASS CLASSIFIER; OBJECTIVE FUNCTION; SVM; SVM LIGHT APPROACH;

PATTERN RECOGNITION;

EID: 33746813627     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2006.05.034     Document Type: Article

References (18)

1. Angulo C., Parra X., and Català A. K-SVCR. A support vector machine for multi-class classification. Neurocomputing 55 1-2 (2003) 57-77
2. Hsu C.-W., and Lin C.-J. A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Networks 13 2 (2002) 415-425
3. K. Duan, S.S. Keerthi, Which is the best multi-class SVM method? An empirical study, Proceedings of the Sixth International Workshop, MCS 2005, Seaside, CA, USA, June 13-15, 2005, pp. 278-285.
4. Lee Y., Lin Y., and Wahba G. Multi-category support vector machines: theory and application to the classification of microarray data and satellite radiance data. J. Am. Statist. Assoc. Theory Methods 99 465 (2004) 67-81
5. D. Anguita, S. Ridella, D. Sterpi, A new method for multi-class support vector machines. Proceedings of the IEEE International Joint Conference on Neural Networks, IJCNN 2004, Budapest, Hungary, July 2004, pp. 412-417.
6. Sebald D.J., and Bucklew J.A. Support vector machines and the multiple hypothesis test problem. IEEE Trans. Signal Process. 49 11 (2001) 2865-2872
7. Bredensteiner E.J., and Bennett K.P. Multi-category classification by support vector machines. Comput. Optim. Appl. 12 (1999) 53-79
8. Van Gestel T., Suykens J.A.K., Lanckriet G., et al. Multi-class LS-SVMs: moderated outputs and coding-decoding schemes. Neural Process. Lett. 15 1 (2002) 45-58
9. David V., and Sánchez A. Advanced support vector machines and kernel methods. Neurocomputing 55 1-2 (2003) 5-20
10. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2 (1998) 121-167
11. Joachims T. Making large-scale SVM learning practical. In: Schölkopf B., Burges C.J.C., and Smola A.J. (Eds). Advances in Kernel Methods-Support Vector Learning (1998), MIT Press, Cambridge, USA 41-56
12. Karaçalí B., Ramanath R., and Snyder W.E. A comparative analysis of structural risk minimization by support vector machines and nearest neighbor rule. Pattern Recognition Lett. 25 1 (2004) 63-71
13. Wang W., and Xu Z. A heuristic training for support vector regression. Neurocomputing 61 (2004) 259-275
14. Maruyama K.-I., Maruyama M., Miyao H., and Nakano Y. A method to make multiple hypotheses with high cumulative recognition rate using SVMs. Pattern Recognition 37 2 (2004) 241-251
15. Gao J.B., Gunn S.R., and Harris C.J. Mean field method for the support vector machine regression. Neurocomputing 50 (2003) 391-405
16. González L., Angulo C., Velasco F., and Català A. Unified dual for bi-class SVM approaches. Pattern Recognition 38 10 (2005) 1772-1774
17. Platt J.C., Cristianini N., and Taylor J.S. Large margin DAGs for multi-class classification. In: Solla S.A., Leen T.K., and Miiller K.-R. (Eds). Advances in Neural Information Processing Systems vol. 12 (2000), MIT Press, Cambridge, MA 547-553
18. Vapnik V., Golowich S., and Smola A. Support vector method for function approximation, regression estimation, and signal processing. In: Mozer M., Jordan M., and Petsche T. (Eds). Advances in Neural Information Processing Systems vol. 9 (1997), MIT Press, Cambridge, MA 281-287