Despite the recent progress towards efficient multiple kernel learning (MKL),the structured output case remains an open research front. Current approachesinvolve repeatedly solving a batch learning problem, which makes theminadequate for large scale scenarios. We propose a new family of onlineproximal algorithms for MKL (as well as for group-lasso and variants thereof),which overcomes that drawback. We show regret, convergence, and generalizationbounds for the proposed method. Experiments on handwriting recognition anddependency parsing testify for the successfulness of the approach.
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