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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Van Belle, V., Pelckmans, K., Suykens, J. A. K., & Van Huffel, S. (2009). MINLIP: Efficient learning of transformation models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5768 LNCS, pp. 60–69). https://doi.org/10.1007/978-3-642-04274-4_7
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