Abstract
The ordinal regression problem or ordination have mixed features of both, the classification and the regression problem, so it can be seen as an independent problem class. The particular behaviour of this sort of problem should be explicitly considered by the learning machines working on it. In this paper the ordination problem is fomulated from the viewpoint of a recently defined learning architecture based on support vectors, the K-SVCR learning machine, specially developed to treat with multiple classes. In this study its definition is compared to other existing results in the literature.
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References
Angulo, C., Català, A.: K-SVCR. Am ulti-class support vector machine. Proc. ECML’00, Lecture Notes in Artificial Intelligence 1810 (2000) 31–38.
Burges, C. J. C.:A Tutorial on support vector machines for pattern recognition. Data Mining And Knowledge Discovery 2 (1998) 1–47.
Herbrich, R., Graepel, T., Obermayer, K.:Support vector learning for ordinal regression. Proc. ICANN’99 (1999) 97–102.
Phillips, P. J.: Support vector machines applied to face recognition. In Advances in NIPS 11 (1999).
Sobel, M.: Complete ranking procedures with appropriate loss functions. Communications in Statistics-Theory and Methods 19(12) (1990) 4525–4544.
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Angulo, C., Català, A. (2001). Ordinal Regression with K-SVCR Machines. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_79
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DOI: https://doi.org/10.1007/3-540-45720-8_79
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