Steppest descent analysis for unregularized linear prediction with strictly convex penalties
published: Jan. 25, 2012, recorded: December 2011, views: 202
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This manuscript presents a convergence analysis, generalized from a study of boosting, of unregularized linear prediction. Here the empirical risk — incorporating strictly convex penalties composed with a linear term — may fail to be strongly convex, or even attain a minimizer. This analysis is demonstrated on linear regression, decomposable objectives, and boosting.
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