An all-at-once unimodal SVM approach for ordinal classification

Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010

Volumn , Issue , 2010, Pages 59-64

  Pinto Da Costa, Joaquim F ^a   Sousa, Ricardo ^b   Cardoso, Jaime S ^b  

^a UNIVERSITY OF PORTO   (Portugal)

^b UNIVERSITY OF PORTO   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

ALL-AT-ONCE; GENERALISATION; MULTI-CLASSIFICATION; ORDINAL CLASSIFICATION; POSTERIORI; UNIMODAL;

SUPPORT VECTOR MACHINES;

EID: 79952408292     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2010.16     Document Type: Conference Paper

References (18)

1. N. Delannay and M. Verleysen, "Collaborative filtering with interlaced generalized linear models," Neurocomputing, vol. 71, no. 7-9, pp. 1300-1310, 2008.
2. A. Tagliafico, G. Tagliafico, S. Tosto, F. Chiesa, C. Martinoli, L. E. Derchi, and M. Calabrese, "Mammographic density estimation: Comparison among bi-rads categories, a semi-automated software and a fully automated one," The Breast, vol. 18, no. 1, pp. 35-40, 2009.
3. P. McCullagh, "Regression models for ordinal data," Journal Royal Statistical Society, Series B, vol. 42, pp. 109-142, 1980.
4. G. Tutz, "Generalized semiparametrically structured ordinal models," Biometrics, vol. 59, pp. 263-273, 2003.
5. T. J. Hastie and R. J. Tibshirani, "Generalized additive models," Monographs on Statisics and Applied Probability, vol. 43, pp. 297-318, 1990.
6. R. Herbrich, T. Graepel, and K. Obermayer, "Regression models for ordinal data: A machine learning approach," Technical University of Berlin, Tech. Rep., 1999.
7. V. N. Vapnik, Statistical Learning Theory. Wiley-Interscience, 1998.
8. E. Frank and M. Hall, "A simple approach to ordinal classification," in EMCL '01: Proceedings of the 12th European Conference on Machine Learning. London, UK: Springer-Verlag, 2001, pp. 145-156.
9. E. L. Allwein, R. E. Schapire, and Y. Singer, "Reducing multiclass to binary: a unifying approach for margin classifiers," Journal Machine Learning Research, vol. 1, pp. 113-141, 2001.
10. A. Shashua and A. Levin, "Ranking with large margin principle: Two approaches," in Advances in Neural Information Processing Systems 15, Thrun and K. Obermayer, Eds. Cambridge, MA: MIT Press, 2003, pp. 937-944.
11. S. Kotsiantis, "Cascade generalisation for ordinal problems," International Journal of Artificial Intelligence and Soft Computing, vol. 2, pp. 46-57(12), 4 April 2010.
12. J. S. Cardoso and J. F. P. da Costa, "Learning to classify ordinal data: the data replication method," Journal of Machine Learning Research, vol. 8, pp. 1393-1429, 2007.
13. J. F. P. da Costa, H. Alonso, and J. S. Cardoso, "The unimodal model for the classification of ordinal data," Neural Networks, vol. 21, pp. 78-91, 2008.
14. J. F. P. da Costa and J. S. Cardoso, "Classification of ordinal data using neural networks," Lecture Notes in Artificial Intelligence, vol. 3720, pp. 690-697, 2005.
15. J. S. Cardoso and M. J. Cardoso, "Towards an intelligent medical system for the aesthetic evaluation of breast cancer conservative treatment," Artificial Intelligence in Medicine, vol. 40, pp. 115-126, 2007.
16. S. Abe, Support Vector Machines for Pattern Classification (Advances in Pattern Recognition). Springer, 2005.
17. K. Crammer and Y. Singer, "On the Learnability and Design of Output Codes for Multiclass Problems," Machine Learning, vol. 47, no. 2-3, pp. 201-233, 2002.
18. I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun, "Support vector learning for interdependent and structured output spaces," in International Conference on Machine Learning, 2004.