Bundle methods for machine learning
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]
|Collections||ANU Research Publications|
|Source:||Advances in Neural Information Processing Systems 20: Proceedings of the 2007 Conference|
|01_Smola_Bundle_methods_for_machine_2008.pdf||296.25 kB||Adobe PDF||Request a copy|
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