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Bundle methods for machine learning

Smola, Alexander; Vishwanathan, S; Le, Quoc


We present a globally convergent method for regularized risk minimization problems. Our method applies to Support Vector estimation, regression, Gaussian Processes, and any other regularized risk minimization setting which leads to a convex optimization problem. SVMPerf can be shown to be a special case of our approach. In addition to the unified framework we present tight convergence bounds, which show that our algorithm converges in O(1=ε) steps to ε precision for general convex problems and...[Show more]

CollectionsANU Research Publications
Date published: 2008
Type: Conference paper
Source: Advances in Neural Information Processing Systems 20: Proceedings of the 2007 Conference


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