Kernel Learning for Novelty Detection
published: Dec. 20, 2008, recorded: December 2008, views: 661
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We consider kernel learning for one-class Support Vector Machines. We consider a mix of 2- and 1-norms of the individual weight vector norms allowing control of the sparsity of the resulting kernel combination. The resulting optimisation can be solved eﬃciently using a coordinate gradient method. We consider an application to automatically detecting the appropriate metric for a guided image search task.
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