Stein Refinement. Putting these altogether, we arrive at a robust and efficient variational learning method for multiclass kernel machines with extremely accurate approximation. Moreover, our formulation enables efficient learning of kernel parameters and hyperparameters which robustifies the proposed method against data uncertainties. The extensive experiments show that without tuning any parameter on modest quantities of data our method obtains comparable accuracy to LIBSVM, a well-known implementation of SVM, and outperforms other baselines, while being able to seamlessly scale with large-scale datasets.">

Robust Variational Learning for Multiclass Kernel Models With Stein Refinement (original) (raw)

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