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• W130653142 abstract "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." @default.
• W130653142 created "2016-06-24" @default.
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• W130653142 date "2013-06-16" @default.
• W130653142 modified "2023-09-23" @default.
• W130653142 title "Local Deep Kernel Learning for Efficient Non-linear SVM Prediction" @default.
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