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Spotlight
Bundle Methods for Machine Learning
Alexander Smola · Vishwanathan S V N · Quoc V Le
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 $O(1/\epsilon)$ steps to $\epsilon$ precision for general convex problems and in $O(\log \epsilon)$ steps for continuously differentiable problems. We demonstrate in experiments the performance of our approach.
Author Information
Alexander Smola (Amazon)
**AWS Machine Learning**
Vishwanathan S V N (National ICT Australia)
Quoc V Le (Stanford)
Related Events (a corresponding poster, oral, or spotlight)
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2007 Poster: Bundle Methods for Machine Learning »
Tue. Dec 4th 06:30 -- 06:40 PM Room
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