24 search results - page 1 / 5 » On the Generalization Ability of Online Strongly Convex Prog... |
NIPS
2008
13 years 6 months ago
2008
This paper examines the generalization properties of online convex programming algorithms when the loss function is Lipschitz and strongly convex. Our main result is a sharp bound...
ICML
2003
IEEE
14 years 5 months ago
2003
IEEE
Convex programming involves a convex set F Rn and a convex cost function c : F R. The goal of convex programming is to find a point in F which minimizes c. In online convex prog...
CVPR
2010
IEEE
14 years 28 days ago
2010
IEEE
Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled approach to combine multiple cues, and to obtain state-of-theart perform...
NIPS
2008
13 years 6 months ago
2008
We describe a primal-dual framework for the design and analysis of online strongly convex optimization algorithms. Our framework yields the tightest known logarithmic regret bound...
NIPS
2007
13 years 6 months ago
2007
We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori k...