Steppest descent analysis for unregularized linear prediction with strictly convex penalties
published: Jan. 25, 2012, recorded: December 2011, views: 192
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Download slides: nipsworkshops2011_telgarsky_penalties_01.pdf (247.7 KB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !