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NECO
2007

Support Vector Ordinal Regression

13 years 3 months ago
Support Vector Ordinal Regression
In this paper, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these optimization problems is linear in the number of training samples. The SMO algorithm is adapted for the resulting optimization problems; it is extremely easy to implement and scales efficiently as a quadratic function of the number of examples. The results of numerical experiments on some benchmark and real-world data sets, including applications of ordinal regression to information retrieval, verify the usefulness of these approaches.
Wei Chu, S. Sathiya Keerthi
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where NECO
Authors Wei Chu, S. Sathiya Keerthi
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