Name
Abstract | ||
|---|---|---|
This paper studies a risk minimization approach to estimate a transformation model from noisy observations. It is argued that transformation models are a natural candidate to study ranking models and ordinal regression in a context of machine learning. We do implement a structural risk minimization strategy based on a Lipschitz smoothness condition of the transformation model. Then, it is shown how the estimate can be obtained efficiently by solving a convex quadratic program with O (n ) linear constraints and unknowns, with n the number of data points. A set of experiments do support these findings. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04274-4_7 | ICANN (1) |
Keywords | Field | DocType |
support vector machine,ordinal regression,structural risk minimization,machine learning | Computer science,Empirical risk minimization,Lipschitz continuity,Artificial intelligence,Quadratic programming,Structural risk minimization,Mathematical optimization,Ranking,Least squares support vector machine,Pattern recognition,Support vector machine,Ordinal regression,Machine learning | Conference |
Volume | ISSN | Citations |
5768 | 0302-9743 | 2 |
PageRank | References | Authors |
0.44 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| V. Van Belle | 1 | 77 | 8.55 |
| Kristiaan Pelckmans | 2 | 251 | 27.44 |
| Johan A K Suykens | 3 | 2346 | 241.14 |
| Sabine Van Huffel | 4 | 1058 | 149.38 |