A Convex Approach to Learning the Ridge based on CV
نویسنده
چکیده
This paper1 advances results in model selection by relaxing the task of optimally tuning the regularization parameter in a number of algorithms with respect to the classical cross-validation performance criterion as a convex optimization problem. The proposed strategy differs from the scope of e.g. generalized cross-validation (GCV) as it concerns the efficient optimization, not the individual evaluation of the model selection criterion.
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