An improved ordinal regression approach with sum-of-margin principle

Proceedings - 2010 6th International Conference on Natural Computation, ICNC 2010

Volumn 2, Issue , 2010, Pages 853-857

  Sun, Bing Yu ^a   Zhang, Xiao Ming ^a   Li, Wen Bo ^a  

^a INSTITUTE OF INTELLIGENT MACHINES   (China)

Author keywords

Fixed margin; Ordinal regression; Sum of margin; Support vector

Indexed keywords

EID: 78149335156     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNC.2010.5583269     Document Type: Conference Paper

References (10)

1. R.H erbrich, T. Graepel and K. Obermayer, Large margin rank boundaries for ordinal regression. Advances in Large Margin Classifiers, pp.115-132, the MIT Press, 2000.
2. K. Crammer and Y. Singer. Pranking with ranking. In T. G. Dietterich, S. Becker, and Z. Ghahramani, eds., Advances in Neural Information Processing Systems 14, vol. 1, pp. 641-647. MIT Press, 2002.
3. A. Shashua and A. Levin. Ranking with large margin principle: Two approaches. In S. Becker, S. Thrun, and K. Obermayer, eds., Advances in Neural Information Processing Systems 15, pp. 961-968. MIT Press, 2003.
4. W. Chu and S. S. Keerthi. New approaches to support vector ordinal regression. In L. D. Raedt and S. Wrobel, eds., ICML 2005: Proceedings of the 22nd International Conference on Machine Learning, pp. 145-152. Omnipress, 2005.
5. L. Lin and H.-T. Lin, Ordinal Regression by Extended Binary Classification, In B. Scholkopfet al., eds., Advances in Neural Information Processing Systems 19: Proceedings of the 2006Conference (NIPS 2006), 865-872, MIT Press, 2007.
6. J. S. Cardoso, J. F. Pinto da Costa, Learning to Classify Ordinal Data: The Data Replication Method, Journal of Machine Learning Research, vol.8, pp1393-1429, 2007.
7. V. Vapnik, The Nature of Statistical Learning Theory. New York: Wiley, 1998.
8. K.-R. Muller, S. Mika, G. Ratsch, K. Tsuda and B. Scholkopf, :An introduction to Kernel-based Learning Algorithms. IEEE Trans. Neural Network, 12(2), pp181-201, 2001.
9. J.C. Platt, Fast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods-Support Vector Learing. The MIT Press, pp185-208, 1999.
10. S. Keerthi, S. K. Shevade, C. Bhattacharyya and K. R. K. Murthy, Improvements to Platts SMO algorithm for SVM classifier design. Neural Computation, 13, 637-649, 2001.