Steppest descent analysis for unregularized linear prediction with strictly convex penalties

author: Matus Telgarsky, Department of Computer Science and Engineering, UC San Diego
published: Jan. 25, 2012,   recorded: December 2011,   views: 4413


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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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