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2007
ACM

A primal-dual perspective of online learning algorithms

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A primal-dual perspective of online learning algorithms
We describe a novel framework for the design and analysis of online learning algorithms based on the notion of duality in constrained optimization. We cast a sub-family of universal online bounds as an optimization problem. Using the weak duality theorem we reduce the process of online learning to the task of incrementally increasing the dual objective function. The amount by which the dual increases serves as a new and natural notion of progress for analyzing online learning algorithms. We are thus able to tie the primal objective value and the number of prediction mistakes using the increase in the dual.
Shai Shalev-Shwartz, Yoram Singer
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where ML
Authors Shai Shalev-Shwartz, Yoram Singer
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