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Steppest descent analysis for unregularized linear prediction with strictly convex penalties

Steppest descent analysis for unregularized linear prediction with strictly convex penalties

This video was recorded at NIPS Workshops, Sierra Nevada 2011. 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.... Show More
Keywords:
videolectures, ocwc, oec
Disciplines:
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More about this material

Material Type:
Presentation
Date Added to MERLOT:
February 10, 2015
Date Modified in MERLOT:
February 10, 2015
Author:
Submitter:
The Open Education Consortium
Primary Audience:
College General Ed, College Lower Division, College Upper Division
Technical Format:
Video
Mobile Compatibility:
Not specified at this time
Language:
English
Cost Involved:
No
Source Code Available:
No
Creative Commons:
Creative Commons License
This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States

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