Kernel Learning for Novelty Detection

author: John Shawe-Taylor, Centre for Computational Statistics and Machine Learning, University College London
published: Dec. 20, 2008,   recorded: December 2008,   views: 661
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Description

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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