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Robustness and Regularization of Support Vector Machines
This video was recorded at NIPS Workshop on Optimization for Machine Learning, Whistler 2008. We consider a robust classification problem and show that standard regularized SVM is a special case of our formulation, providing an explicit link between reg- ularization and robustness. At the same time, the physical connection of noise and robustness suggests the potential for a broad new family of robust classification algorithms. Finally, we show that robustness is a fundamental property of classi- fication algorithms, by re-proving consistency of support vector machines using only robustness arguments (instead of VC dimension or stability).
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
