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CANCELLED: Local Deep Kernel Learning for Efficient Non-linear SVM Prediction

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If you have a question about this talk, please contact Zoubin Ghahramani .

CANCELLED

THIS TALK HAS BEEN CANCELLED : Our objective is to speed up non-linear SVM prediction while maintaining classification accuracy above an acceptable limit. We generalize Localized Multiple Kernel Learning so as to learn a tree-based primal feature embedding which is high dimensional and sparse. Primal based classification decouples prediction costs from the number of support vectors and our tree-structured features efficiently encode non-linearities while speeding up prediction exponentially over the state-of-the-art. We develop routines for optimizing over the space of tree-structured features and efficiently scale to problems with more than half a million training points. Experiments on benchmark data sets reveal that our formulation can reduce prediction costs by more than three orders of magnitude in some cases with a moderate sacrifice in classification accuracy as compared to RBF -SVMs. Furthermore, our formulation leads to better classification accuracies over leading methods.

This talk is part of the Machine Learning @ CUED series.

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